http://2014.igem.org/wiki/index.php?title=Special:Contributions&feed=atom&limit=250&target=Mirelio&year=&month=2014.igem.org - User contributions [en]2024-03-28T23:42:46ZFrom 2014.igem.orgMediaWiki 1.16.5http://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T21:49:16Z<p>Mirelio: </p>
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<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
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<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
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<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
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<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
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The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
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<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
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<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
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<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the AzoDyes and sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of AzoDyes and sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised:<br />
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<img src="https://static.igem.org/mediawiki/2014/8/81/Timecourse.jpg" class="imgsizecorrect"><br />
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The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
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<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. All distributions are between 0 and 1. Drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Three parameters stand out as very constricted, k3, k8, k17 and k18. These are the parameters of the reactions for intake (k3) and secretion (k8) of AzoDyes as well as the intake (k17) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those points in our pathway. So if we increase the rate of intake and secretion of AzoDyes and sulfonated AzoDyes in our synthetic pathway, we could speed up the process of degradation! </p><br />
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<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
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<h3>Azo Reductase</h3><br />
<br />
<p>The mechanism of reductive cleavage can either be thought of a step wise addition of H+ ions and electrons or hydride and H+ ions in concert as pictured below <br />
</p><br />
<br><br />
<img src=“https://static.igem.org/mediawiki/2014/d/d4/AzoRMechanism.png”><br />
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<h3>Laccase and Peroxidases</h3><br />
<br />
<p>Although many papers have touched on these oxidation mechanisms; they tend to skip steps and don’t make entire sense. Examples of this exist in [1]:<br />
</p><br />
<img src=“https://static.igem.org/mediawiki/2014/d/df/Screen_Shot_2014-10-17_at_18.43.08.png”><br />
<p>It’s issues include radicals gaining electrons and remaining radicals. Protons disappearing and more of the like. We have therefore worked hard to create a mechanism that makes sense.<br />
</p><br />
<img src=“https://static.igem.org/mediawiki/2014/0/06/Oxidising_Azo_Pathway_General.png”><br />
<br><br />
<p> Other reactions such as the polymerisation are lacking literature completely and therefore have been modelled as below. The example polymerisation is via the azo reductase product of mordant brown 33. </p><br />
<img src=“https://static.igem.org/mediawiki/2014/1/11/Polymerisationreactionlaccase.png”<br />
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<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T21:48:02Z<p>Mirelio: </p>
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<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
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<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
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The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
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<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
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<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
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<div id="view2"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of AzoDyes and sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised:<br />
<BR>&nbsp;<BR><br />
<img src="https://static.igem.org/mediawiki/2014/8/81/Timecourse.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<BR>&nbsp;<BR><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. All distributions are between 0 and 1. Drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Three parameters stand out as very constricted, k3, k8, k17 and k18. These are the parameters of the reactions for intake (k3) and secretion (k8) of AzoDyes as well as the intake (k17) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those points in our pathway. So if we increase the rate of intake and secretion of AzoDyes and sulfonated AzoDyes in our synthetic pathway, we could speed up the process of degradation! </p><br />
</div><br />
<div id="view3"><br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
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<!---Georgia: paste your content here for chemical mechanism---><br />
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<div id="view6"><br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
</div><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Timecourse.jpgFile:Timecourse.jpg2014-10-17T21:44:23Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/File:Azo_posterior.pngFile:Azo posterior.png2014-10-17T21:34:45Z<p>Mirelio: uploaded a new version of &quot;File:Azo posterior.png&quot;</p>
<hr />
<div></div>Mireliohttp://2014.igem.org/File:Timeseries_Population14.pngFile:Timeseries Population14.png2014-10-17T21:33:35Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T21:30:36Z<p>Mirelio: </p>
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<ul class="tabs"><br />
<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
</ul><br />
<div class="tabcontents"><br />
<br />
<!--- This is the tabs content section ---><br />
<div id="view1"><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
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<i style="color:#F7931E" class="fa fa-flask fa-2x fa-fw"></i><strong><br />
<!--- Title start ---><br />
The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
<!--- Title end ---><br />
</strong></div><div class="body"><br />
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<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
<!--- Content end---><br />
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<!--------- This is the end of the expanding box--------><br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view2"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of AzoDyes and sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. All distributions are between 0 and 1. Drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Three parameters stand out as very constricted, k3, k8, k17 and k18. These are the parameters of the reactions for intake (k3) and secretion (k8) of AzoDyes as well as the intake (k17) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those points in our pathway. So if we increase the rate of intake and secretion of AzoDyes and sulfonated AzoDyes in our synthetic pathway, we could speed up the process of degradation! </p><br />
<BR>&nbsp;<BR><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view3"><br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view4"><br />
<!---Georgia: paste your content here for enzyme kinetics---><br />
<br />
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<div id="view5"><br />
<!---Georgia: paste your content here for chemical mechanism---><br />
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<div id="view6"><br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
</div><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T21:30:06Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
{{:Team:UCL/Template:BioprocessStyles}}<br />
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<div id="TopGapO"></div><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c6/OModeling_Bannero.jpg" width="100%" height="100%" alt="Modelling" /><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<!--- This is the coding for the tabs (ask sanjay before altering this) ---><br />
<ul class="tabs"><br />
<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
</ul><br />
<div class="tabcontents"><br />
<br />
<!--- This is the tabs content section ---><br />
<div id="view1"><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<!-------- This is the beginning of the expanding box--------><br />
<div class="collapse-card"><div class="title"><br />
<i style="color:#F7931E" class="fa fa-flask fa-2x fa-fw"></i><strong><br />
<!--- Title start ---><br />
The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
<!--- Title end ---><br />
</strong></div><div class="body"><br />
<!--- Content start---><br />
<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
<!--- Content end---><br />
</div></div><br />
<!--------- This is the end of the expanding box--------><br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view2"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of AzoDyes and sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. All distributions are between 0 and 1. Drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Three parameters stand out as very constricted, k3, k17 and k18. These are the parameters of the reactions for intake (k3) and secretion (k8) of AzoDyes as well as the intake (k17) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those points in our pathway. So if we increase the rate of intake and secretion of AzoDyes and sulfonated AzoDyes in our synthetic pathway, we could speed up the process of degradation! </p><br />
<BR>&nbsp;<BR><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view3"><br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view4"><br />
<!---Georgia: paste your content here for enzyme kinetics---><br />
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<!---Georgia: paste your content here for chemical mechanism---><br />
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<div id="view6"><br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
</div><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Azo_posterior.pngFile:Azo posterior.png2014-10-17T21:28:06Z<p>Mirelio: uploaded a new version of &quot;File:Azo posterior.png&quot;</p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T21:26:03Z<p>Mirelio: </p>
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<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
</ul><br />
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<div id="view1"><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<!-------- This is the beginning of the expanding box--------><br />
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<i style="color:#F7931E" class="fa fa-flask fa-2x fa-fw"></i><strong><br />
<!--- Title start ---><br />
The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
<!--- Title end ---><br />
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<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
<!--- Content end---><br />
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<!--------- This is the end of the expanding box--------><br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view2"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of AzoDyes and sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. All distributions are between 0 and 1. Drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Three parameters stand out as very constricted, k3, k17 and k18. These are the parameters of the reactions for intake (k3) and secretion (k8) of AzoDyes as well as the intake (k17) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those points in our pathway. So if we increase the rate of intake and secretion of AzoDyes and sulfonated AzoDyes in our synthetic pathway, we could speed up the process of degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view3"><br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view4"><br />
<!---Georgia: paste your content here for enzyme kinetics---><br />
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<!---Georgia: paste your content here for chemical mechanism---><br />
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</div><br />
<div id="view6"><br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
</div><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T21:22:44Z<p>Mirelio: </p>
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<!--- This is the coding for the tabs (ask sanjay before altering this) ---><br />
<ul class="tabs"><br />
<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
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<div id="view1"><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<!-------- This is the beginning of the expanding box--------><br />
<div class="collapse-card"><div class="title"><br />
<i style="color:#F7931E" class="fa fa-flask fa-2x fa-fw"></i><strong><br />
<!--- Title start ---><br />
The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
<!--- Title end ---><br />
</strong></div><div class="body"><br />
<!--- Content start---><br />
<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
<!--- Content end---><br />
</div></div><br />
<!--------- This is the end of the expanding box--------><br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view2"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. All distributions are between 0 and 1. Drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Three parameters stand out as very constricted, k3, k17 and k18. These are the parameters of the reactions for intake of Azodyes (k3) and intake of sulfonated AzoDyes (k17) as well as the secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view3"><br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view4"><br />
<!---Georgia: paste your content here for enzyme kinetics---><br />
<br />
</div><br />
<div id="view5"><br />
<!---Georgia: paste your content here for chemical mechanism---><br />
<br />
</div><br />
<div id="view6"><br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
</div><br />
<!--- aaaand stop! ---><br />
</div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Azo_posterior.pngFile:Azo posterior.png2014-10-17T21:19:31Z<p>Mirelio: uploaded a new version of &quot;File:Azo posterior.png&quot;</p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T20:14:24Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
{{:Team:UCL/Template:BioprocessStyles}}<br />
<html><br />
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<div id="BPimagewrapperHeader"><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/OModeling_Bannero.jpg" width="100%" height="100%" alt="Modelling" /><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<!--- This is the coding for the tabs (ask sanjay before altering this) ---><br />
<ul class="tabs"><br />
<li><a href="#view1">Modelling Degradation</a></li><br />
<li><a href="#view2">Parameter Inference</a></li><br />
<li><a href="#view3">Flux Balance Analysis</a></li><br />
<li><a href="#view4">Enzyme Kinetics</a></li><br />
<li><a href="#view5">Chemical Mechanism</a></li><br />
<li><a href="#view6">References</a></li><br />
</ul><br />
<div class="tabcontents"><br />
<br />
<!--- This is the tabs content section ---><br />
<div id="view1"><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<!-------- This is the beginning of the expanding box--------><br />
<div class="collapse-card"><div class="title"><br />
<i style="color:#F7931E" class="fa fa-flask fa-2x fa-fw"></i><strong><br />
<!--- Title start ---><br />
The Role of Microfluidic Analysis to Evaluate the Scalable Synbio Azo-Remediation Solution<br />
<!--- Title end ---><br />
</strong></div><div class="body"><br />
<!--- Content start---><br />
<a style="width: 50%;float: right;margin-left:2%"><img src="https://static.igem.org/mediawiki/2014/8/81/UCLMFSCJtimeline.jpg" style="max-width: 100%;"></a><br />
We have <a data-tip="true" class="top large" data-tip-content="Design of a complete industrial-scale process application, and testing of module units using customized microfluidic devices." href="javascript:void(0)"><b>designed and tested</b></a> a novel approach to azo-remediation, which allows sustainable and scalable bioprocessing. Our bioprocess integrates elements from <a data-tip="true" class="top large" data-tip-content="Investigation of bioreactor design and performance." href="javascript:void(0)"><b>upstream</b></a> and <a data-tip="true" class="top large" data-tip-content="Identification of downstream processing requirements, and design of a novel immobilisation module." href="javascript:void(0)"><b>downstream</b></a> processing.<br />
<br><br><br />
In order to develop and improve the functionality of our bioprocess, key steps must be tested to quantify <a data-tip="true" class="top large" data-tip-content="Such as flow rates to determine residence time." href="javascript:void(0)"><b>process variables</b></a>, and allow for preliminary mass transfer calculations and detection of azo dye degradation rates.<br />
<br><br><br />
We have created microfluidic prototype devices to test the mixing in our reactors, and to test the performance of our novel immobilisation module, allowing for process optimisation and testing, without the <a data-tip="true" class="top large" data-tip-content="Microfluidic testing maintains low fabrication costs and reagent consumption, ideal for our testing stages." href="javascript:void(0)"><b>burdens</b></a> of expensive pilot scale testing.<br />
<br><br><br />
The process testing timeline demonstrates that effective microfluidic testing can be used in replacement to conventional small-scale testing approaches. This is ideal for our project, especially when optimising whole unit operations.<br />
<!--- Content end---><br />
</div></div><br />
<!--------- This is the end of the expanding box--------><br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view2"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view3"><br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
</div><br />
<div id="view4"><br />
<!---Georgia: paste your content here for enzyme kinetics---><br />
<br />
</div><br />
<div id="view5"><br />
<!---Georgia: paste your content here for chemical mechanism---><br />
<br />
</div><br />
<div id="view6"><br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<p> Okada, K., Minehira, M., Zhu, X., Suzuki, K., et al. (1997) The ispB gene encoding octaprenyl diphosphate synthase is essential for growth of Escherichia coli. Journal of bacteriology. 179 (9), 3058–3060. </p><br />
<br />
<p> Søballe, B. & Poole, R.K. (1999) Microbial ubiquinones: multiple roles in respiration, gene regulation and oxidative stress management. Microbiology (Reading, England). 145 ( Pt 8)1817–1830 </p><br />
</div><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:47:53Z<p>Mirelio: </p>
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<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<BR>&nbsp;<BR><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:47:25Z<p>Mirelio: </p>
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<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<BR>&nbsp;<BR><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:47:08Z<p>Mirelio: </p>
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<BR>&nbsp;<BR><br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:46:20Z<p>Mirelio: </p>
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<!-- ==========================CONTENT========================== --><br />
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<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (Liepe, 2014) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<br />
</ul><br />
<ul><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:44:15Z<p>Mirelio: </p>
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<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> In our case we used growth rate as the objective to maximise. We performed FBA for the core E.coli metabolism with and without Ubiquinone present. With Ubiquinone present the growth rate was calculated to be 0.98 h<sup> -1 </sup>. Without Ubiquione in the system the growth rate was found to be 0 h<sup> -1 </sup>, indicating that E.coli would not grow and survive without ubiquione. This suggested that silencing the essential genes for Ubiquinone production and supplying it externally would give us control over the survival of the chassis and ultimately allow us to contain it.</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:40:54Z<p>Mirelio: </p>
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</div> <br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test! In order to do that we used Flux Balance Analysis (FBA). FBA is a method that uses the metabolism model of E.coli (see below) and calculates the flow of metabolites through that system that is required to maximise a given obective </p><br />
Ecoli metabolism plotted in Cytoscape (Cline, 2007):<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<br />
</ul><br />
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<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:38:45Z<p>Mirelio: </p>
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<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<p> In order to see whether our xenobiological approach would work we wanted to check whether lack of Ubiquinone would have an effect on the growth rate of the chassis. The literature (Okada 1997 and Soballe, 1999) suggested that Ubiquinone is essential for E.coli growth so we decided to put that to the test!<br />
</p><br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
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</ul><br />
<br />
<br />
<br />
</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:06:58Z<p>Mirelio: </p>
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<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<BR>&nbsp;<BR><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<BR>&nbsp;<BR><br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<BR>&nbsp;<BR><br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<BR>&nbsp;<BR><br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<BR>&nbsp;<BR><br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:06:21Z<p>Mirelio: </p>
<hr />
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<p></p><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR>&nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
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</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:05:37Z<p>Mirelio: </p>
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<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<p></p><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR><BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:04:16Z<p>Mirelio: </p>
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<p></p><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<BR>& nbsp;<BR> <br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:02:59Z<p>Mirelio: </p>
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<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<p></p><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio) . </p> <br />
<br />
<p></p><br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:02:38Z<p>Mirelio: </p>
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<p></p><br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio/) . </p> <br />
<br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T16:01:00Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> There are three ways we can degrade azodyes: using Azoreductase (AzoR), Laccase (Lac) or BsDyp. Azoreductase breaks down AzoDye (AzoD) into two products Laccase breaks down AzoDye as well as the products of the reaction of Azoreductase with AzoDye. BsDyP acts on sulfonated AzoDyes (sAzoD):</p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> In order to model this system we used COPASI. We included equations for gene expression and degradation for each gene in our pathway, as well as the intake and excretion of AzoDyes and sulfonated AzoDyes. The equations we included as well as the parameter assigned to each one are shown below: </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<br />
<br />
<p>Using reasonable parameter values, the simulation showed that the AzoDye is degraded within two days (48 hours). This timeframe agrees with the experimental results!</p><br />
<br />
<li>Simulated timecourse data of Acid Orange AzoDye degradation by Azoreductase, Laccase and BsDyP:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the sulfonated AzoDyes. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally in the future in order to speed up the degradation. To do that we used ABC-SysBio (http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio/) . </p> <br />
<br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. Given a model and data form that model, it computes the most likely parameters that could give rise to that data. We used the model and simulated data we had in order to find out which parameters are restricted in the values they can have in order to achieve that behaviour. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The prior distributions consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the data from the degradation of sulfonated AzoDyes over two days. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the data provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until the distance between the data and the simulations is minimised. The parameter values that gave rise to this final population are called the 'posterior distribution'. This is shown in the figure below: </p> <br />
<br />
<br />
<p>The distribution of values for each parameter are shown in the diagonal. Then drawing a straight line from one parameter to the other, at the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k17 and k18. These are the parameters of the reactions for intake (k13) and secretion (k18) of sulfonated AzoDyes by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we increase the rate of intake and secretion of AzoDyes in our synthetic pathway, we could speed up the process of AzoDye degradation! </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/7/7f/Azo_posterior.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Azo_posterior.pngFile:Azo posterior.png2014-10-17T15:50:04Z<p>Mirelio: uploaded a new version of &quot;File:Azo posterior.png&quot;</p>
<hr />
<div></div>Mireliohttp://2014.igem.org/File:Azo_posterior.pdfFile:Azo posterior.pdf2014-10-17T15:47:28Z<p>Mirelio: uploaded a new version of &quot;File:Azo posterior.pdf&quot;</p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T13:57:27Z<p>Mirelio: </p>
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. </p><br />
<!-- <img src="https://static.igem.org/mediawiki/2014/9/9d/Copasi_screenshot.png" class="imgsizecorrect">--><br />
<br />
<p>The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase, laccase and BsDyP. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T13:55:54Z<p>Mirelio: </p>
<hr />
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<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. </p><br />
<img src="https://static.igem.org/mediawiki/2014/9/9d/Copasi_screenshot.png" class="imgsizecorrect"><br />
<br />
<p>The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase, laccase and BsDyP. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
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<br />
</ul><br />
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<br />
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</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Copasi_values_screenshot.pngFile:Copasi values screenshot.png2014-10-17T13:55:25Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/File:Copasi_screenshot.pngFile:Copasi screenshot.png2014-10-17T13:54:08Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T13:43:42Z<p>Mirelio: </p>
<hr />
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase, laccase and BsDyP. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
<p> This was made using cytoscape</p><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
<h3>References</h3><br />
<br />
<p> Liepe, J., Kirk, P., Filippi, S., Toni, T., et al. (n.d.) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. [Online] 9 (2), 439–456.</p> <br />
<br />
<p> Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N., Singhal M., Xu L., Mendes P. and Kummer U. (2006). COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067-74.</p> <br />
<br />
<p> Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols. [Online] 2 (10), 2366–2382. </p><br />
<br />
<p> Orth, J.D., Thiele, I. & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology. [Online] 28 (3), 245–248. </p><br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T13:23:04Z<p>Mirelio: </p>
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</div> <br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c1/Reactions.jpg" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase, laccase and BsDyP. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
</ul><br />
<ul><br />
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</ul><br />
<br />
<br />
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</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Reactions.jpgFile:Reactions.jpg2014-10-17T13:22:37Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T13:12:25Z<p>Mirelio: </p>
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase, laccase and BsDyP. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/All_enzymes_copasi.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:All_enzymes_copasi.pngFile:All enzymes copasi.png2014-10-17T13:11:29Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-17T07:41:52Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
<html><br />
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<div id="BPimagewrapperHeader"><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/OModeling_Bannero.jpg" width="100%" height="100%" alt="Modelling" /><br />
</div> <br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/5/51/Miriam_Pathway_v3_copy.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
.pageTitle {<br />
height:200px;<br />
width:100%;<br />
background-color:darkgrey;<br />
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display:inline-block;<br />
}<br />
.floater {<br />
float:left;<br />
}<br />
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background-color:white;<br />
padding: 5% 5% 5% 5%;<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Miriam_Pathway_v3_copy.pngFile:Miriam Pathway v3 copy.png2014-10-17T07:41:12Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-16T09:37:56Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
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<div id="BPimagewrapperHeader"><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/OModeling_Bannero.jpg" width="100%" height="100%" alt="Modelling" /><br />
</div> <br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/d/d1/Miriam_Pathway.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
Ecoli metabolism<br />
<img src="https://static.igem.org/mediawiki/2014/a/a0/Ecoli_hairball.png" class="imgsizecorrect"><br />
<br />
Core metabolism map used for FBA<br />
<img src="https://static.igem.org/mediawiki/2014/1/10/Ecoli_core_flux_before.png" class="imgsizecorrect"><br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
.pageTitle {<br />
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background-color:darkgrey;<br />
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display:inline-block;<br />
}<br />
.floater {<br />
float:left;<br />
}<br />
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background-color:white;<br />
padding: 5% 5% 5% 5%;<br />
font-size:90%;<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Ecoli_core_flux_before.pngFile:Ecoli core flux before.png2014-10-16T09:37:27Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/File:Ecoli_hairball.pngFile:Ecoli hairball.png2014-10-16T09:35:21Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-10-16T09:25:43Z<p>Mirelio: </p>
<hr />
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</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in the figure below: </p><br />
<img class="imgsizecorrect" src="https://static.igem.org/mediawiki/2014/d/d1/Miriam_Pathway.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI. We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
<br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
.pageTitle {<br />
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display:inline-block;<br />
}<br />
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float:left;<br />
}<br />
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background-color:white;<br />
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font-size:90%;<br />
}<br />
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}<br />
.imgsizecorrect {<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-24T11:15:10Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
<html><br />
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<div><h3>Modelling</h3></div><br />
<div><!--- This is the Bit that describes the team and its logo DONT TOUCH--><br />
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<div class="floater"><h4 class="minimyzr" style="margin:0px;">Modelling Team</h4></div><br />
</div><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<img src="https://static.igem.org/mediawiki/2014/d/d1/Miriam_Pathway.png"> <br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
<br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
.pageTitle {<br />
height:200px;<br />
width:100%;<br />
background-color:darkgrey;<br />
padding-top:50px;<br />
display:inline-block;<br />
}<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/File:Miriam_Pathway.pngFile:Miriam Pathway.png2014-09-24T11:14:07Z<p>Mirelio: </p>
<hr />
<div></div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-23T16:43:06Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
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<div><h3>Modelling</h3></div><br />
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<div class="floater"><h4 class="minimyzr" style="margin:0px;">Modelling Team</h4></div><br />
</div><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
<br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
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</ul><br />
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</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-23T15:52:16Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
<html><br />
<div class="pageTitle"><br />
<div><h3>Modelling</h3></div><br />
<div><!--- This is the Bit that describes the team and its logo DONT TOUCH--><br />
<div class="floater"><img src="https://static.igem.org/mediawiki/2014/1/1d/Team_Icons-04.png" height="50px" width="50px" style="margin-right:10px;"></img></div><br />
<div class="floater"><h4 class="minimyzr" style="margin:0px;">Modelling Team</h4></div><br />
</div><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<br />
https://drive.google.com/?usp=docs_home&authuser=0&usp=docs_home&urp=http://www.google.co.uk/accounts/Logout2?hl%3Den_US%26#folders/0B57PaTgXCxoTd0NNRWQtY0hmdkk<br />
<br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
<br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
.pageTitle {<br />
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background-color:darkgrey;<br />
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float:left;<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-23T15:46:17Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
<html><br />
<div class="pageTitle"><br />
<div><h3>Modelling</h3></div><br />
<div><!--- This is the Bit that describes the team and its logo DONT TOUCH--><br />
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<div class="floater"><h4 class="minimyzr" style="margin:0px;">Modelling Team</h4></div><br />
</div><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
<br />
<br />
<br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
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float:left;<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-23T15:45:29Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
<html><br />
<div class="pageTitle"><br />
<div><h3>Modelling</h3></div><br />
<div><!--- This is the Bit that describes the team and its logo DONT TOUCH--><br />
<div class="floater"><img src="https://static.igem.org/mediawiki/2014/1/1d/Team_Icons-04.png" height="50px" width="50px" style="margin-right:10px;"></img></div><br />
<div class="floater"><h4 class="minimyzr" style="margin:0px;">Modelling Team</h4></div><br />
</div><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
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<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<br />
<br />
<br />
<li>Parameter estimations for pathway model have been found for desired behaviours (using approximate Bayesian computation, ABC SysBio).</li><br />
<li>Graphs of results.</li><br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
<!-- =========================STOP========================== --><br />
</div><!-- This is the css of the page. Dont change it unless you have consulted with Lewis or Adam about what your changing--><br />
<style><br />
/*=======PAGE HEADER=======*/<br />
.pageTitle {<br />
height:200px;<br />
width:100%;<br />
background-color:darkgrey;<br />
padding-top:50px;<br />
display:inline-block;<br />
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float:left;<br />
}<br />
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.textArena {<br />
background-color:white;<br />
padding: 5% 5% 5% 5%;<br />
font-size:90%;<br />
}<br />
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width:100%;<br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-23T15:44:35Z<p>Mirelio: </p>
<hr />
<div>{{:Team:UCL/Template:headerx}}<br />
<html><br />
<div class="pageTitle"><br />
<div><h3>Modelling</h3></div><br />
<div><!--- This is the Bit that describes the team and its logo DONT TOUCH--><br />
<div class="floater"><img src="https://static.igem.org/mediawiki/2014/1/1d/Team_Icons-04.png" height="50px" width="50px" style="margin-right:10px;"></img></div><br />
<div class="floater"><h4 class="minimyzr" style="margin:0px;">Modelling Team</h4></div><br />
</div><br />
</div> <br />
<div class="textArena"><!--- This is the were your text goes, play with it but dont change the class names--><br />
<div class="textTitle"></div><br />
<br />
<!-- ==========================CONTENT========================== --><br />
<!-- Titles go in a <h1>TITLE GOES HERE</h1> and h1 is this biggest title and h6 is the smallest. all paragraphs go in <p>paragraph goes here</p> tags. Images go in as <img src="url of image here"> and to upload an image go to https://2014.igem.org/Special:Upload. Upload the image then click on the image which takes you to a page with only an image on it. The url of the image is the image you want to use. Use google and ask Lewis and Adam as much as you want--><br />
<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Parameter estimations for pathway model have been found for desired behaviours (using approximate Bayesian computation, ABC SysBio).</li><br />
<li>Graphs of results.</li><br />
</ul><br />
<ul><br />
<br />
<br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
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{{:Team:UCL/Template:footerx}}</div>Mireliohttp://2014.igem.org/Team:UCL/Science/ModelTeam:UCL/Science/Model2014-09-23T15:43:21Z<p>Mirelio: </p>
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<div><h3>Modelling</h3></div><br />
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<br />
<h3>Overview</h3><br />
<p> We modelled our synthetic pathway as seen in Fig. ??. </p><br />
<p> Using a sample of parameters we simulated our synthetic pathway, using COPASI (Figure ??). We are showing the pathways for one of the azo-dyes here, methyl red. The simulation showed that methyl red is degraded rapidly by laccase (orange) and azoreductase (green). </p><br />
<br />
<h3>Parameter inference</h3><br />
<br />
<p> We wanted to see which part of the pathway is the bottleneck in degrading the azo-dyes as fast as possible. So we analysed the parameters of our model to see which one is the most constrained, which could give us an insight on which one to tweak experimentally. To do that we used ABC-SysBio (Barnes, 2011) </p><br />
<h6> Approximate Bayesian Computation </h6><br />
<br />
<p> Approximate Bayesian Computation (ABC) is a method that utilises Bayesian statistics for parameter inference in synthetic biology. An overview of the way it works can be found in Figure ??. </p><br />
<p> To use ABC-SysBio we had to make an SBML file describing our model and write an xml input file. The input file contains values for initial conditions of each species in our model, as well as prior distributions for each parameter. The priors consist of a range of values for each parameter, from which the algorithm will sample values. The input file also contains the time course of one of the species involved, against which each simulation will be compared. We used the simulation results of methyl red degradation. </p><br />
<br />
<p>ABC-SysBio samples a value for each parameter from the priors and using the initial conditions provided, simulates the model. The resulting time course is compared to the desired behaviour provided, and if the distance between the two is greater than a threshold e, the sampled parameter set is rejected. This is repeated for 100 sets of samples, consisting of one population. The sets that were accepted are then perturbed by a small amount and then a new population is sampled from the perturbed sets. This process is repeated until a final e is reached, when the distance between the simulated and desired time courses is minimal. The parameter values that gave rise to this final population are called the 'posterior distribution', and is a subset of the prior distribution defined initially. </p> <br />
<br />
<br />
<p> The results of ABC-SysBio are shown in Figure ??. The distribution of values for each parameter are shown in the diagonal. At the point where the two meet, the two parameters have been plotted against each other in a density contour plot. Two parameters stand out as very constricted, k3 and k8. These are the parameters of the reactions for intake (k3) and secretion (k8) of methyl red by the cell. This shows that the bottleneck happens at those two points in our pathway. So if we were two increase the rate of intake and secretion of azo-dye in our synthetic pathway, we could increase the efficiency of azo-dye degradation </p><br />
<br />
<br />
<h3> Flux Balance Analysis </h3><br />
<ul> <br />
<br />
<li> Equations for pathway model.</li><br />
<img src="https://static.igem.org/mediawiki/2014/0/0c/AzoD_deg_react_pwpt.png" class="imgsizecorrect"><br />
<li>Parameter estimations for pathway model have been found for desired behaviours (using approximate Bayesian computation, ABC SysBio).</li><br />
<li>Graphs of results.</li><br />
</ul><br />
<ul><br />
<li>Simulated timecourse data of methyl red degradation by azoreductase and laccase. Created using Copasi:</li><br />
<br />
<img src="https://static.igem.org/mediawiki/2014/c/c6/Methyl_red_timecourse_no_event.png" class="imgsizecorrect"><br />
<br />
<br />
<li>Posterior distribution of model parameters</li> <br />
<img src="https://static.igem.org/mediawiki/2014/5/54/Azo_posterior_2.png" class="imgsizecorrect"><br />
<br />
</ul><br />
<br />
<br />
<br />
</p><br />
<br />
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{{:Team:UCL/Template:footerx}}</div>Mirelio