Team:Dundee/Modeling

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             <p class="lead">Maths.. maths is fun!</p>
             <p class="lead">Maths.. maths is fun!</p>
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            <li class="list-group-item"><a href="#0" class="">Initial planning and cloning strategy</a>
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                <li><a href="#2"></a></li>
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            <li class="list-group-item"><a href="#1" class="">Building the PQS sensor</a>
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            <li class="list-group-item"><a href="#2" class="">Characterisation</a>  
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             <h2 id="0">Modeling and Analysis of Signaling Pathways</h2>
             <h2 id="0">Modeling and Analysis of Signaling Pathways</h2>
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             <hr>
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             <h2 id="1">Methodology</h2>
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             <h2 id="1">Introduction</h2>
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The models for each system were developed using three different approaches. As shown in figure 1 each of the approaches; ordinary differential equations (ODEs), stochastic simulation algorithm (SSA) and NetLogo, provided a different understanding of each system.
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In order to help analyze, construct and optimise the biochemical pathways in the Lung Ranger, we used a variety of mathematical tools to create algorithms and simulations.This allowed us to accelerate the development and testing of various hypotheses.
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<b>Methodology</b>
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As shown in figure 1 each of the approaches; ordinary differential equations (ODEs), stochastic simulation algorithm (SSA) and NetLogo, provided a different understanding of each system.
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             <h2 id="2">Sigmoidal Expression of mCherry in PQS System </h2>
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             <h2 id="2">PQS</h2>
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When the PQS system was induced with synthetic PQS, no mCherry was expressed and so the wet team sought the advice of the dry team to find out why and how the situation could be resolved.
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<b>Objectives</b>
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We wanted to find a method of increasing the expression of mCherry in our engineered PQS system. 
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<b>Results</b>
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<b>Sigmoidal Expression of mCherry in PQS System</b>
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When our PQS system was induced with synthetic PQS, no mCherry was expressed. We attempted to use mathematical modelling to find out why and how the situation could be resolved.  
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             <img  data-alt-src="https://static.igem.org/mediawiki/2014/4/4d/Varying_PQS.png"  
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             <img  data-alt-src="https://static.igem.org/mediawiki/2014/8/85/PQS_fig1-2.png"  
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Figure 2 shows how the concentration of mCherry increases over time. The general trend is that at low concentrations the rate of production of mCherry is slow and at high concentrations the production is fast.
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Fig 1. shows how the concentration of mCherry increases over time. The general trend is that the rate of expression of mCherry is proportional to PQS signal strength. Most striking is the rapid switch from low expression to high expression as the signal is increased.  
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             <img  data-alt-src="https://static.igem.org/mediawiki/2014/c/c7/PQS_switch.png"  
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When the gradients of d[mcherry]/dt were plotted against PQS concentration, figure 3, the result was a sigmoidal curve. This implies that for PQS concentrations below 0.1𝜇M there should be low mCherry production and above 1𝜇M there should be high mCherry production.
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When the gradients of mCherry were plotted against PQS concentration, Fig 2, the result was a sigmoidal curve with sharp transition located between signal values of 0.1µM and 1µM.  Below this, expression was effectively zero, and above it, expression was uniformly high.
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These results suggest that the levels of synthetic PQS investigated in our engineered pathway were too low and that the ten fold increases used in the experiments would yield no measurable change in mCherry expression provided the signal level was below the threshold predicted by the model. To investigate this further we went on to carry out stochastic simulations of the system.
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We predict that the low expression could correlate to P.aeruginosa being in an acute planktonic state and the high expression to a chronic biofilm state.
 
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<b>Stochastic Simulations Elucidates the “Switch” Behaviour</b>
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The lab experiments resulted in no measurable mCherry expression in the PQS system. However, our ODE model predicts that even for very low levels some mCherry is expressed. These levels could represent below-measurable output, thus are essentially zero. We investigated this further at the “single cell” level.
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We constructed a stochastic simulation algorithm (SSA) in order to visualise signal-response at the single cell level. Using the SSA we were able to simulate  mCherry expression of 1000 cells to varying PQS signals over a period of one cell cycle.
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            <img  data-alt-src="https://static.igem.org/mediawiki/2014/1/12/PQS_fig3.png"
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            <h2 id="3">Stochastic confirms “switch” behaviour</h2>
 
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Once the PQS “switch” was found using the sigmoidal graphs for low and high production of mCherry, we were able to construct stochastic simulations in order to visualise this with regards to a single cell model. The simulations were able to show how varying the PQS signal would promote various level of mCherry expression over one cell cycle.
 
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At first sight, the stochastic mean in A appeared too low in comparison with the example realisations. However, further analysis revealed that this low mean is caused by many realisations in which the expression of mCherry was zero (i.e. the promoter never fired). As shown in Figure 4, in 77% of the realisations for 0.02μM PQS, no mCherry was produced. Increasing the PQS concentration through the previously predicted threshold increased the expression rate  to 90%. For PQS concentrations greater than 2μM, all realisations produced mCherry in the given time frame.  
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What is interesting to note is that once expression started, mCherry was produced at essentially the same rate, as shown by the parallel trajectories in Fig 3We can conclude that a major rate limiting step in the system is the probability of the initiation of mCherry expression.  
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The stochastic mean in A seems low in comparison to the rest of the realisations which implies there were many reactions which didn’t express any mCherry.  Figure 5 reveals that in 77% of the realisations for 0.02μM of PQS, no mCherry was produced.  Increasing the PQS concentration by 10-fold, increases the number of reactions fired to 90%. For PQS concentrations greater than 2μM all the reactions fire in the given time frame.  
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What is interesting to note is that once the reactions have started they produce mCherry at the same rate, shown by the parallel realisations in figure 4.  We can conclude that a rate limiting step in the system is the probability that the reaction for mCherry expression starts.
 
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These stochastic simulations enhanced the deterministic results. The switch behaviour between low and high levels mCherry expression resulted in predicted corresponding molecule numbers shown in Table 2.
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<b>Increasing promoter numbers increases mCherry expression</b>
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We concluded from the SSA that firing of the mCherry promoter was a major rate limiting step, but  wanted to provide the wet team with  a more understanding of  how to increase mCherry production. Subsequently,  after testing several model l hypotheses, it was found that increasing the number of promoters had the greatest impact on the expression of mCherry (varying other experimentally controllable parameters were predicted to have  essentially no effect on expression level).
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When the number of promoters in the model  was increased by 10-fold (30 to 300) there was a corresponding 10-fold increase in the “high” mCherry expression, as shown in Fig 5.  We suggested that increasing the number of promoters would be the only practical method of significantly increasing mCherry expression. However it was subsequently discovered that the number of promoters in our chassis could not be increased significantly as high numbers proved toxic.
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<b>Conclusions</b>
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Our models revealed a distinct switch behaviour in the signal-response curve for mCherry expression.  Using this analysis we put forward the hypothesis that  the synthetic PQS concentrations used in our experiments were not sufficiently high and although these experiments were conducted using a range of synthetic PQS levels, this range corresponded to fold changes in the low-response region shown in Fig. 2.
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The predicted switch behaviour has important and positive consequences for the L.A.S.S.O.(link to lasso page) -  we predict that the mCherry output level would be distinctly bimodal, avoiding the necessity for sensitive calibration of the detecting device - low signals would result in approximately no response, whereas  supercritical signals would induce maximal response from the sample.
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            <h2 id="4">BDSF</h2>
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<b>Objectives</b>
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To investigate why GFP expression was high in the absence of signal in our engineered BDSF system.
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<b>Results</b>
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<b>BDSF-Induced Phosphorylation of BCAM0228[P] Mediates GFP Expression</b>
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We constructed models similar to those used in the PQS system to investigate the signal-response behaviour of the BDSF system. Our results verified what we had expected to happen. Phosphorylation of BCAM0228 is induced by BDSF binding to a cell receptor.  BCAM0228[P] then activated the expression of GFP through the engineered <i>cvld</i> promoter. In the absence of a BDSF signal, BCAM0228 remained in its unphosphorylated form and GFP expression was repressed.  Increasing the signal induced an increased GFP response (Fig 1).
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<b>BCAM0228 is Phosphorylated by Unknown Compound</b>
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The stochastic results correlate with the deterministic results as the drastic change between low and high level production of mCherry remains between 0.2𝜇M and 2𝜇M as shown in table 2.  
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Our experimental results revealed that in the absence of BDSF, the cblD promoter was active in our engineered system and hence GFP expression upregulated.  
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            <img  data-alt-src="https://static.igem.org/mediawiki/2014/1/1c/BDSF_fig2.png"
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src="https://static.igem.org/mediawiki/2014/1/1c/BDSF_fig2.png" />                             
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From our system of equations we know that the expression of GFP is dependant on the levels of BCAM0228[P]. We hypothesised that one possible mechanism by which our cell could be expressing GFP in the absence of signal was by the presence of BCAM0228[P] independent of BDSF-binding mediated phosphorylation. This suggests that an unknown compound could be phosphorylating BCAM0228 in our engineered cells.
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Comparing Fig 2 to Fig 1, it is clear that the model predicts that the expression of GFP is higher when BCAM0228 is being constitutively phosphorylated than in the “normal” case. Moreover, we are also able to conclude that the model predicts that the presence of BDSF has very little effect on the overall expression of GFP when the constitutive phosphorylation process is operating.
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<b>Conclusions</b>
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Our models predict that an as yet unknown agent phosphorylates BCAM0228 in the absence of a BDSF signal and that in fact the level of the BDSF signal has little quantifiable effect on GFP expression in our engineered cells.
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             <h2 id="4">Stochastic confirms “switch” behaviour</h2>
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             <h2 id="4">DSF</h2>
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<b>Objectives</b>
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To investigate why GFP expression was high in the absence of signal in our engineered DSF system.
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<b>Results</b>
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<b>DSF-Induced Phosphorylation of RPFG Mediates GFP Expression</b>
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We constructed models similar to those used in the PQS system to investigate the signal-response behaviour of the DSF system. Our results verified what we had expected to happen. Phosphorylation of RpfG is induced by DSF binding to a cell receptor. RpfG[P] then degrades c-di-GMP which relieves the inhibition of Clp. Clp then activated the expression of GFP through the engineered manA promoter. In the absence of a DSF signal, RpfG remained in its unphosphorylated form and GFP expression was repressed.
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<b>DSF-Independent Activation of the <i>manA</i> Promoter</b>
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Our experimental results revealed that in the absence of DSF, the <i>manA</i> promoter was active in our engineered system and hence GFP expression upregulated.
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Like the BDSF pathway, the DSF system contains a phosphorelay system.  Our first hypothesis, therefore was that the expression of GFP was dependent on signal-independent phosphorylation of RpfG.
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            <img  data-alt-src="https://static.igem.org/mediawiki/2014/d/dc/DSF_fig1.png"
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However, as shown in Fig 1 the model predicted  that increasing the level of  phosphorylated RpfG would have  no significant effect on the production of GFP in our engineered cells.
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Our second hypothesis was that enhanced inhibition of Clp by elevated levels of c-di-GMP could repress GFP expression. However, on setting c-di-GMP levels in the model to a high level, GFP was still expressed (albeit at a lower low level) , Fig 2. This can be explained by Clp having a higher binding affinity for the promoter than c-di-GMP.  Since both these reactions are reversible, there will still  be sufficient Clp free in the cytoplasm to activate GFP expression.
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The model predictions so far still did not explain the high expression levels reported by the wet team. Thus bringing us to our third hypothesis - our cells were over-expressing Clp.
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An analysis of our model for the DSF system revealed that the steady state levels of free Clp and c-di-GMP are only dependent on their (constitutive) rate of production and degradation (and not dependent on the Clp - c-di-GMP binding affinities nor the promoter binding affinity).  As shown in Fig 3 we see that the model predict that  an over-expression of Clp results in a corresponding high level of GFP production.
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<b>Conclusion</b>
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Our models were used to test different plausible hypotheses for the DFS-signal independent expression of GFP in our engineered cells. We conclude that over-expression of Clp could be responsible for the experimental observations.
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Latest revision as of 15:56, 11 October 2014

Dundee 2014

Modeling

Maths.. maths is fun!

Modeling and Analysis of Signaling Pathways


Introduction

In order to help analyze, construct and optimise the biochemical pathways in the Lung Ranger, we used a variety of mathematical tools to create algorithms and simulations.This allowed us to accelerate the development and testing of various hypotheses.

Methodology
As shown in figure 1 each of the approaches; ordinary differential equations (ODEs), stochastic simulation algorithm (SSA) and NetLogo, provided a different understanding of each system.


PQS

Objectives
We wanted to find a method of increasing the expression of mCherry in our engineered PQS system.

Results
Sigmoidal Expression of mCherry in PQS System
When our PQS system was induced with synthetic PQS, no mCherry was expressed. We attempted to use mathematical modelling to find out why and how the situation could be resolved.

After constructing a series of ordinary differential equations (full derivation can be found in the appendix) we established the following relationship between PQS (Se) and mCherry:

Equation (1) was then analysed in MAPLE for varying PQS concentrations using the parameters in table 1.

Fig 1. shows how the concentration of mCherry increases over time. The general trend is that the rate of expression of mCherry is proportional to PQS signal strength. Most striking is the rapid switch from low expression to high expression as the signal is increased.

When the gradients of mCherry were plotted against PQS concentration, Fig 2, the result was a sigmoidal curve with sharp transition located between signal values of 0.1µM and 1µM. Below this, expression was effectively zero, and above it, expression was uniformly high.

These results suggest that the levels of synthetic PQS investigated in our engineered pathway were too low and that the ten fold increases used in the experiments would yield no measurable change in mCherry expression provided the signal level was below the threshold predicted by the model. To investigate this further we went on to carry out stochastic simulations of the system.

Stochastic Simulations Elucidates the “Switch” Behaviour
The lab experiments resulted in no measurable mCherry expression in the PQS system. However, our ODE model predicts that even for very low levels some mCherry is expressed. These levels could represent below-measurable output, thus are essentially zero. We investigated this further at the “single cell” level.

We constructed a stochastic simulation algorithm (SSA) in order to visualise signal-response at the single cell level. Using the SSA we were able to simulate mCherry expression of 1000 cells to varying PQS signals over a period of one cell cycle.

At first sight, the stochastic mean in A appeared too low in comparison with the example realisations. However, further analysis revealed that this low mean is caused by many realisations in which the expression of mCherry was zero (i.e. the promoter never fired). As shown in Figure 4, in 77% of the realisations for 0.02μM PQS, no mCherry was produced. Increasing the PQS concentration through the previously predicted threshold increased the expression rate to 90%. For PQS concentrations greater than 2μM, all realisations produced mCherry in the given time frame.

What is interesting to note is that once expression started, mCherry was produced at essentially the same rate, as shown by the parallel trajectories in Fig 3. We can conclude that a major rate limiting step in the system is the probability of the initiation of mCherry expression.

These stochastic simulations enhanced the deterministic results. The switch behaviour between low and high levels mCherry expression resulted in predicted corresponding molecule numbers shown in Table 2.

Increasing promoter numbers increases mCherry expression
We concluded from the SSA that firing of the mCherry promoter was a major rate limiting step, but wanted to provide the wet team with a more understanding of how to increase mCherry production. Subsequently, after testing several model l hypotheses, it was found that increasing the number of promoters had the greatest impact on the expression of mCherry (varying other experimentally controllable parameters were predicted to have essentially no effect on expression level).

When the number of promoters in the model was increased by 10-fold (30 to 300) there was a corresponding 10-fold increase in the “high” mCherry expression, as shown in Fig 5. We suggested that increasing the number of promoters would be the only practical method of significantly increasing mCherry expression. However it was subsequently discovered that the number of promoters in our chassis could not be increased significantly as high numbers proved toxic.

Conclusions
Our models revealed a distinct switch behaviour in the signal-response curve for mCherry expression. Using this analysis we put forward the hypothesis that the synthetic PQS concentrations used in our experiments were not sufficiently high and although these experiments were conducted using a range of synthetic PQS levels, this range corresponded to fold changes in the low-response region shown in Fig. 2.

The predicted switch behaviour has important and positive consequences for the L.A.S.S.O.(link to lasso page) - we predict that the mCherry output level would be distinctly bimodal, avoiding the necessity for sensitive calibration of the detecting device - low signals would result in approximately no response, whereas supercritical signals would induce maximal response from the sample.


BDSF

Objectives
To investigate why GFP expression was high in the absence of signal in our engineered BDSF system.

Results
BDSF-Induced Phosphorylation of BCAM0228[P] Mediates GFP Expression
We constructed models similar to those used in the PQS system to investigate the signal-response behaviour of the BDSF system. Our results verified what we had expected to happen. Phosphorylation of BCAM0228 is induced by BDSF binding to a cell receptor. BCAM0228[P] then activated the expression of GFP through the engineered cvld promoter. In the absence of a BDSF signal, BCAM0228 remained in its unphosphorylated form and GFP expression was repressed. Increasing the signal induced an increased GFP response (Fig 1).


BCAM0228 is Phosphorylated by Unknown Compound
Our experimental results revealed that in the absence of BDSF, the cblD promoter was active in our engineered system and hence GFP expression upregulated.

From our system of equations we know that the expression of GFP is dependant on the levels of BCAM0228[P]. We hypothesised that one possible mechanism by which our cell could be expressing GFP in the absence of signal was by the presence of BCAM0228[P] independent of BDSF-binding mediated phosphorylation. This suggests that an unknown compound could be phosphorylating BCAM0228 in our engineered cells.

Comparing Fig 2 to Fig 1, it is clear that the model predicts that the expression of GFP is higher when BCAM0228 is being constitutively phosphorylated than in the “normal” case. Moreover, we are also able to conclude that the model predicts that the presence of BDSF has very little effect on the overall expression of GFP when the constitutive phosphorylation process is operating.

Conclusions
Our models predict that an as yet unknown agent phosphorylates BCAM0228 in the absence of a BDSF signal and that in fact the level of the BDSF signal has little quantifiable effect on GFP expression in our engineered cells.


DSF

Objectives
To investigate why GFP expression was high in the absence of signal in our engineered DSF system.

Results
DSF-Induced Phosphorylation of RPFG Mediates GFP Expression
We constructed models similar to those used in the PQS system to investigate the signal-response behaviour of the DSF system. Our results verified what we had expected to happen. Phosphorylation of RpfG is induced by DSF binding to a cell receptor. RpfG[P] then degrades c-di-GMP which relieves the inhibition of Clp. Clp then activated the expression of GFP through the engineered manA promoter. In the absence of a DSF signal, RpfG remained in its unphosphorylated form and GFP expression was repressed.

DSF-Independent Activation of the manA Promoter
Our experimental results revealed that in the absence of DSF, the manA promoter was active in our engineered system and hence GFP expression upregulated.

Like the BDSF pathway, the DSF system contains a phosphorelay system. Our first hypothesis, therefore was that the expression of GFP was dependent on signal-independent phosphorylation of RpfG.

However, as shown in Fig 1 the model predicted that increasing the level of phosphorylated RpfG would have no significant effect on the production of GFP in our engineered cells.

Our second hypothesis was that enhanced inhibition of Clp by elevated levels of c-di-GMP could repress GFP expression. However, on setting c-di-GMP levels in the model to a high level, GFP was still expressed (albeit at a lower low level) , Fig 2. This can be explained by Clp having a higher binding affinity for the promoter than c-di-GMP. Since both these reactions are reversible, there will still be sufficient Clp free in the cytoplasm to activate GFP expression.

The model predictions so far still did not explain the high expression levels reported by the wet team. Thus bringing us to our third hypothesis - our cells were over-expressing Clp.

An analysis of our model for the DSF system revealed that the steady state levels of free Clp and c-di-GMP are only dependent on their (constitutive) rate of production and degradation (and not dependent on the Clp - c-di-GMP binding affinities nor the promoter binding affinity). As shown in Fig 3 we see that the model predict that an over-expression of Clp results in a corresponding high level of GFP production.

Conclusion
Our models were used to test different plausible hypotheses for the DFS-signal independent expression of GFP in our engineered cells. We conclude that over-expression of Clp could be responsible for the experimental observations.

References