Team:ATOMS-Turkiye/Modelling

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<li>These are the four chemical reactions which represent each process.</li>
<li>These are the four chemical reactions which represent each process.</li>
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<p style="text-indent:"25px">The symbol declaration is:</p>
<p style="text-indent:"25px">The symbol declaration is:</p>
<li>X1 : HIF1-α</li>
<li>X1 : HIF1-α</li>
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<li>Using the SimBiology toolbox for Matlab, we designed a framework of the hypoxia inducible promoter system (Figure 1). The processes of binding, dissociation of transcription factor, expression of luciferase in hypoxic/normoxic conditions, and the degradation of luciferase were included in framework.</li>
<li>Using the SimBiology toolbox for Matlab, we designed a framework of the hypoxia inducible promoter system (Figure 1). The processes of binding, dissociation of transcription factor, expression of luciferase in hypoxic/normoxic conditions, and the degradation of luciferase were included in framework.</li>
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<p><a href="http://placehold.it/600x600">Figure1: </a>The diagram of reactions was designed in the SimBiology toolbox for MATLAB.</p>
<p><a href="http://placehold.it/600x600">Figure1: </a>The diagram of reactions was designed in the SimBiology toolbox for MATLAB.</p>
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<h3>What did it show?</h3>
<h3>What did it show?</h3>
<ul><li>Before running the model, we had to decide on how the hypoxia response element affects gene expression The pCMV promoter (distincted from HRE) expressed approximately 0.15 nM and the [HRE- pCMV] complex expressed 1.86 nM of luciferase in 12 hours.  In our simulations, we show that adding hypoxia response element in our promoter design increases the protein expression approximately 20 times more in hypoxic conditions.</li></ul>
<ul><li>Before running the model, we had to decide on how the hypoxia response element affects gene expression The pCMV promoter (distincted from HRE) expressed approximately 0.15 nM and the [HRE- pCMV] complex expressed 1.86 nM of luciferase in 12 hours.  In our simulations, we show that adding hypoxia response element in our promoter design increases the protein expression approximately 20 times more in hypoxic conditions.</li></ul>
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<ul><li>This data was then used to design an experiment that could be performed in the lab to verify the model. You can visit our <a href="https://2014.igem.org/Team:ATOMS-Turkiye/Results">results page</a> to see the comparison.</li></ul>
<ul><li>This data was then used to design an experiment that could be performed in the lab to verify the model. You can visit our <a href="https://2014.igem.org/Team:ATOMS-Turkiye/Results">results page</a> to see the comparison.</li></ul>

Revision as of 20:33, 15 October 2014

Modelling

Overview

  • We have divided our models into two levels. In the gene level, we aimed to gain an insight of the gene expression dynamics of our entire circuit via using hypoxia inducible promoters and hypoxia response elements. We also expected to gain knowledge about how our designed promoter works, which includes sensitive parts which are HRE and NFKβ for ROS and O2 respectively. These parts will work under hypoxic and normoxic conditions during ischemia and reperfusion. We also tried to characterize our parts further and analyze the experimental data.
  • In the protein level, we proposed the multi-compartment model to analyze the changes of SOD, GPX and aprotinin levels which are the antioxidant enzymes able to degrade ROS present in the cells. On the other hand, we tried to calculate the level of tPA transported from the endothelial cell to blood during hypoxia. By using two separate sensitive promoter systems, we aimed to regulate the release of tPA synthesized by our engineered endothelial cell.

    • Gene Expression Dynamics

    Analysis of the problem

  • Firstly, we built a model to help us understand the gene expression dynamics of the whole circuit. In our promoter design, we added hypoxia response element to the upstream of our effector protein gene. In hypoxic conditions, HIF1-α protein levels are high enough to translocate it into the nucleus and form a transcriptional complex with the HIF1-β. We decided not to include a separate step for the dimerization and binding processes to hypoxia response element (HRE). We assumed the HIF1-α level as a constant in hypoxia which does not change dynamically. HIF1-α/ HIF1-β complex can bind to HRE which then activates the transcription of our gene interest. These are tPA, SOD, GPX, aprotinin or luciferase. In addition, CMV promoter has a leaky luciferase expression without HIF1-α. Therefore, we assumed that luciferase expression from HRE + CMV + Luciferase without HIF1-α is equal to the expression of CMV + Luciferase in the normoxic condition.
  • These are the four chemical reactions which represent each process.

    • The symbol declaration is:

  • X1 : HIF1-α
  • Y : HRE- pCMV
  • X1Y : HIF1-α + [HRE- pCMV] Complex
  • X2 : Interested protein (in model, luciferase)
  • X3 : Only pCMV Promoter (distincted from HRE)

    • Finally, we assigned each process with a reaction rate constant.

    How did we build it?

  • Using the SimBiology toolbox for Matlab, we designed a framework of the hypoxia inducible promoter system (Figure 1). The processes of binding, dissociation of transcription factor, expression of luciferase in hypoxic/normoxic conditions, and the degradation of luciferase were included in framework.

    • Figure1: The diagram of reactions was designed in the SimBiology toolbox for MATLAB.

  • After designing the basic framework of our model, we were required to establish mathematical equations for each reaction withan appropriate rate constants. These equations and the corresponding values are shown below in Tables 1 and 2.

    • What did it show?

    • Before running the model, we had to decide on how the hypoxia response element affects gene expression The pCMV promoter (distincted from HRE) expressed approximately 0.15 nM and the [HRE- pCMV] complex expressed 1.86 nM of luciferase in 12 hours. In our simulations, we show that adding hypoxia response element in our promoter design increases the protein expression approximately 20 times more in hypoxic conditions.
    • This data was then used to design an experiment that could be performed in the lab to verify the model. You can visit our results page to see the comparison.
    Design BioBricks

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