Difference between revisions of "Team:Oxford/Protein Based Model"

 
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<div style = "margin-top: 100px"></div>
 
<div style = "margin-top: 100px"></div>
  
<h1 class = "text-center">DNA-Based System Models</h1>
+
<h1 class = "text-center">Protein-Based System Models</h1>
  
 
<center><img class="img-responsive" width="200px;" src="https://static.igem.org/mediawiki/2017/1/1d/T--oxford--drylablogo.png"></center>
 
<center><img class="img-responsive" width="200px;" src="https://static.igem.org/mediawiki/2017/1/1d/T--oxford--drylablogo.png"></center>
  
 
<h2 class = "text-center">Introduction</h2>
 
<h2 class = "text-center">Introduction</h2>
<p>We created a kinetic model to simulate the dynamics of our cell free DNA-based system and to inform the design process. Using this model, we made changes to our original design, most notably the addition of an amplification step to increase anticoagulant production. We modelled the system mechanistically to make it as accurate as possible, using mass action kinetics and Michaelis-Menten kinetics to model the reactions. The goal of this model was to answer the following questions: </p>
+
<p>We developed a number of models for the protein based system to simulate its dynamics. We used this model to inform us of the effectiveness of using a <a href="https://2017.igem.org/Team:Oxford/Design">positive feedback loop</a>, the difference between using a singly-inhibited and doubly-inhibited coil, and also the possibility of using different types of coiled-coils to improve the performance of our system.</p>
<ol>  
+
<p>When designing this system, we were aware of the potential of more <a href="https://2017.igem.org/Team:Oxford/Results_DNA">false positives</a>, as the inhibitory coil could unfold without being cleaved by Cruzipain. This allows the two coiled-coils that are now uninhibited to associate and the resulting split TEV protease might activate the system. It is our goal to quantify this effect.</p>
  <li>How fast is hirudin produced?</li>
+
  <li>How leaky is the TetR promoter? What is the foldchange between a negative and positive test?</li>
+
  <li>Difference between producing hirudin directly and producing TEV to cleave inactivated hirudin?</li>
+
</ol>
+
  
  
 
<h2 class = "text-center">Methodology</h2>
 
<h2 class = "text-center">Methodology</h2>
<p>To model the biochemical reactions, we formulated a system of ordinary differential equations (ODEs) that are solved in MATLAB.</p>
+
<p>The model simulates the cleavage by Cruzipain using Michaelis-Menten kinetics, including the competitive inhibition of multiple identical substrates. The association and dissociation rates of the coiled-coils are estimated from the dissociation constant. The split TEV protease fragments are assumed to have no effect on the association and dissociation of the coiled coils.</p>
<p>The Cell-free DNA system is modelled mechanistically by using dissociation constants found in literature for TetR-TetO binding and RNAP-pTet binding. TetR dimerisation is modelled to introduce TetR monomers that may act to competitively inhibit Cruzipain in cleaving the TetR dimer. Furthermore, ribosomes are modelled to decay over time in the cell-free extract to saturate protein expression at around 2-3h. Because only dissociation constants are obtained, certain rate constants are estimated from the equilibrium constants.</p>
+
 
<p>The following parameters were used:</p>
 
<p>The following parameters were used:</p>
 +
 
<table id = "para-table" class = "table table-hover">
 
<table id = "para-table" class = "table table-hover">
 
   <thead>
 
   <thead>
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       <td>\(k_{cat,sTEV}\)</td>
 
       <td>\(k_{cat,sTEV}\)</td>
 
       <td>\(0.222\)</td>
 
       <td>\(0.222\)</td>
       <td></td>
+
       <td>C & D</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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       <td>\(K_{m,sTEV}\)</td>
 
       <td>\(K_{m,sTEV}\)</td>
 
       <td>\(121*10^{-6}\)</td>
 
       <td>\(121*10^{-6}\)</td>
       <td></td>
+
       <td>C & D</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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       <td>\(\Delta G_{A:B}\)</td>
 
       <td>\(\Delta G_{A:B}\)</td>
 
       <td>\(-6.1\)</td>
 
       <td>\(-6.1\)</td>
       <td></td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
 
       <td>Dissociation Constant of A coil and B coil</td>
 
       <td>Dissociation Constant of A coil and B coil</td>
 
       <td>\(K_{d,A:B}\)</td>
 
       <td>\(K_{d,A:B}\)</td>
       <td>\(e^{\frac{\Delta G_{A:B}}{(R*T)}=e^{\frac{-6.1}{1.99*10^{-3}*(273+25)}}=0.0038\)</td>
+
       <td>\(e^{\frac{\Delta G_{A:B}}{R*T}}=e^{\frac{-6.1}{1.99*10^{-3}*(273+25)}}=0.0038\)</td>
       <td></td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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       <td>\(k_{d,A:B}\)</td>
 
       <td>\(k_{d,A:B}\)</td>
 
       <td>\(K_{d,A:B}*k_{a,A:B}=3.8*10^{-7}\)</td>
 
       <td>\(K_{d,A:B}*k_{a,A:B}=3.8*10^{-7}\)</td>
       <td>E</td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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       <td>\(k_{a,A:B}\)</td>
 
       <td>\(k_{a,A:B}\)</td>
 
       <td>\(10^4\)</td>
 
       <td>\(10^4\)</td>
       <td></td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
       <td>Empirical Adjusted Ratio for A:Inhibitory Coil and B based on Experimental Data</td>
+
       <td>Empirical Adjusted Ratio for A:Inhibitory Coil and B Dissociation Constant based on Experimental Data</td>
 
       <td>\(R_{A:B^*,B}\)</td>
 
       <td>\(R_{A:B^*,B}\)</td>
 
       <td>\(32\)</td>
 
       <td>\(32\)</td>
       <td></td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
       <td>Empirical Adjusted Ratio for A and A:B based on Experimental Data</td>
+
       <td>Empirical Adjusted Ratio for A and A:B Dissociation Constant based on Experimental Data</td>
 
       <td>\(R_{A,A:B}\)</td>
 
       <td>\(R_{A,A:B}\)</td>
 
       <td>\(17\)</td>
 
       <td>\(17\)</td>
       <td></td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
       <td>Empirical Adjusted Ratio for A:Inhibitory Coil and A:B based on Experimental Data</td>
+
       <td>Empirical Adjusted Ratio for A:Inhibitory Coil and A:B Dissociation Constant based on Experimental Data</td>
 
       <td>\(R_{A:B^*,A:B}\)</td>
 
       <td>\(R_{A:B^*,A:B}\)</td>
 
       <td>\(1000\)</td>
 
       <td>\(1000\)</td>
       <td></td>
+
       <td>B</td>
 
     </tr>
 
     </tr>
 
   </tbody>
 
   </tbody>
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     <tr>
 
     <tr>
 
       <td>N Terminal SplitTEV A B* with B* cleaved</td>
 
       <td>N Terminal SplitTEV A B* with B* cleaved</td>
       <td>\(V300_cleaved\)</td>
+
       <td>\(V300_{cleaved}\)</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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     <tr>
 
     <tr>
 
       <td>V200 anchored OMV (sterically-hindered)</td>
 
       <td>V200 anchored OMV (sterically-hindered)</td>
       <td>\(V200_OMV\)</td>
+
       <td>\(V200_{OMV}\)</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
 
       <td>V300 anchored OMV (sterically-hindered)</td>
 
       <td>V300 anchored OMV (sterically-hindered)</td>
       <td>\(V300_OMV\)</td>
+
       <td>\(V300_{OMV}\)</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
 
       <td>V400 anchored OMV (sterically-hindered)</td>
 
       <td>V400 anchored OMV (sterically-hindered)</td>
       <td>\(V400_OMV\)</td>
+
       <td>\(V400_{OMV}\)</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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     <tr>
 
     <tr>
 
       <td>SplitTEV with the inhibitory coil is cleaved (V300_cleaved + V400) i</td>
 
       <td>SplitTEV with the inhibitory coil is cleaved (V300_cleaved + V400) i</td>
       <td>\(splitTEV_clean\)</td>
+
       <td>\(splitTEV_{clean}\)</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
 
       <td>SplitTEV with unfolded inhibitory coil (V300 + V400)</td>
 
       <td>SplitTEV with unfolded inhibitory coil (V300 + V400)</td>
       <td>\(splitTEV_coil\)</td>
+
       <td>\(splitTEV_{coil}\)</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
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     <tr>
 
     <tr>
 
       <td>V200_OMV + V300_OMV + V400_OMV + V300</td>
 
       <td>V200_OMV + V300_OMV + V400_OMV + V300</td>
       <td>\(substrates_cruzipain\)</td>
+
       <td>\(substrates_{cruzipain}\)</td>
 
     </tr>
 
     </tr>
  
 
     <tr>
 
     <tr>
 
       <td>V200_OMV + V300_OMV + V400_OMV + V300</td>
 
       <td>V200_OMV + V300_OMV + V400_OMV + V300</td>
       <td>\(substrates_splitTEV\)</td>
+
       <td>\(substrates_{splitTEV}\)</td>
 
     </tr>
 
     </tr>
 
   </tbody>
 
   </tbody>
 
</table>
 
</table>
<p>The following reactions were modelled:</p>
+
<p>The following reactions were modelled for single inhibitory coil:</p>
 
<p>Cruzipain Cleavage Reactions</p>
 
<p>Cruzipain Cleavage Reactions</p>
$$ 1. V200_OMV + Cruzipain \leftrightharpoons V200_OMV:Cruzipain \to Hirudin + Cruzipain $$
+
$$ 1. V200_{OMV} + Cruzipain \leftrightharpoons V200_{OMV}:Cruzipain \to Hirudin + Cruzipain $$
$$ 2. V300_OMV + Cruzipain \leftrightharpoons V300_OMV:Cruzipain \to V300 + Cruzipain $$
+
$$ 2. V300_{OMV} + Cruzipain \leftrightharpoons V300_{OMV}:Cruzipain \to V300 + Cruzipain $$
$$ 3. V400_OMV + Cruzipain \leftrightharpoons V400_OMV:Cruzipain \to V400 + Cruzipain $$
+
$$ 3. V400_{OMV} + Cruzipain \leftrightharpoons V400_{OMV}:Cruzipain \to V400 + Cruzipain $$
$$ 4. V300 + Cruzipain \leftrightharpoons V300:Cruzipain \to V300_cleaved + Cruzipain $$
+
$$ 4. V300 + Cruzipain \leftrightharpoons V300:Cruzipain \to V300_{cleaved} + Cruzipain $$
 
<p>splitTEV Cleavage Reactions</p>
 
<p>splitTEV Cleavage Reactions</p>
$$ 1. V200_OMV + splitTEV \leftrightharpoons V200_OMV:splitTEV \to Hirudin + splitTEV$$
+
$$ 1. V200_{OMV} + splitTEV \leftrightharpoons V200_{OMV}:splitTEV \to Hirudin + splitTEV$$
$$ 2. V300_OMV + splitTEV \leftrightharpoons V300_OMV:splitTEV \to V300 + splitTEV$$
+
$$ 2. V300_{OMV} + splitTEV \leftrightharpoons V300_{OMV}:splitTEV \to V300 + splitTEV$$
$$ 3. V400_OMV + splitTEV \leftrightharpoons V400_OMV:splitTEV \to V400_OMV + splitTEV $$
+
$$ 3. V400_{OMV} + splitTEV \leftrightharpoons V400_{OMV}:splitTEV \to V400 + splitTEV $$
$$ 4. V300 + splitTEV \leftrightharpoons V300:splitTEV \to V300_cleaved + splitTEV$$
+
$$ 4. V300 + splitTEV \leftrightharpoons V300:splitTEV \to V300_{cleaved} + splitTEV$$
 
<p>Coiled-Coil Interactions</p>
 
<p>Coiled-Coil Interactions</p>
$$ 1. V300_cleaved + V400 \leftrightharpoons splitTEV_clean$$
+
$$ 1. V300_{cleaved} + V400 \leftrightharpoons splitTEV_{clean}$$
$$ 2. V300 + V400 \leftrightharpoons splitTEV_coil $$
+
$$ 2. V300 + V400 \leftrightharpoons splitTEV_{coil} $$
 +
 
 +
<p>The following reactions were modelled for double inhibitory coil:</p>
 +
<p>Cruzipain Cleavage Reactions</p>
 +
$$ 1. V200_{OMV} + Cruzipain \leftrightharpoons V200_{OMV}:Cruzipain \to Hirudin + Cruzipain $$
 +
$$ 2. V300_{OMV} + Cruzipain \leftrightharpoons V300_{OMV}:Cruzipain \to V300 + Cruzipain $$
 +
$$ 3. V400_{OMV} + Cruzipain \leftrightharpoons V400_{OMV}:Cruzipain \to V400 + Cruzipain $$
 +
$$ 4. V300 + Cruzipain \leftrightharpoons V300:Cruzipain \to V300_{cleaved} + Cruzipain $$
 +
<p>splitTEV Cleavage Reactions</p>
 +
$$ 1. V200_{OMV} + splitTEV \leftrightharpoons V200_{OMV}:splitTEV \to Hirudin + splitTEV$$
 +
$$ 2. V300_{OMV} + splitTEV \leftrightharpoons V300_{OMV}:splitTEV \to V300 + splitTEV$$
 +
$$ 3. V400_{OMV} + splitTEV \leftrightharpoons V400_{OMV}:splitTEV \to V400 + splitTEV $$
 +
$$ 4. V300 + splitTEV \leftrightharpoons V300:splitTEV \to V300_{cleaved} + splitTEV$$
 +
$$ 5. V400 + splitTEV \leftrightharpoons V300:splitTEV \to V400_{cleaved} + splitTEV$$
 +
<p>Coiled-Coil Interactions</p>
 +
$$ 1. V300_{cleaved} + V400 \leftrightharpoons splitTEV_{v400coil}$$
 +
$$ 2. V300 + V400 \leftrightharpoons splitTEV_{bothcoils} $$
 +
$$ 3. V300 + V400_{cleaved} \leftrightharpoons splitTEV_{v300coil}$$
 +
$$ 4. V300_{cleaved} + V400_{cleaved} \leftrightharpoons splitTEV_{clean} $$
  
 
<p>These reactions were modelled in ODEs and simulated using the ode15s function at an absolute tolerance of 10-30 and a relative tolerance of 10-7.</p>
 
<p>These reactions were modelled in ODEs and simulated using the ode15s function at an absolute tolerance of 10-30 and a relative tolerance of 10-7.</p>
  
  
<h3 class = "text-center">1. Production of hirudin</h3>
+
<h2>Single Inhibitory Coil or Double Inhibitory Coil</h2>
<p>For this simulation, we wanted to know how fast a cell-free system can produce hirudin. This is very important for us to determine if our system will be able to prevent blood coagulation. Blood clots in 5-10 minutes when taken out of the body, so our system will need to produce enough hirudin in less than that time.</p>
+
<p>We wanted to know if using a single inhibitory coil system or a double inhibitory coil system would give the best result. The main benefit of a double inhibitory coil system is that it is less likely for both inhibitory coil to unfold and cause the coiled-coils to dimerise. As shown by Shekhawat et al., a doubly inhibited coiled-coil has a 1040 fold-difference in output in the absence and presence of a TEV protease, whereas a single inhibited coiled-coil only has a 22 fold-difference. Hence, we wanted to see how a double inhibitory coil system would affect the performance of a <a href="https://2017.igem.org/Team:Oxford/Design">system</a>.</p>
<p>To determine the production rate, we measured the time it takes to produce a threshold level of hirudin. We decided to use 1.3 μM of hirudin as the amount needed to prevent blood coagulation. (Markwardt, 1992)</p>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/b/b1/T--oxford--proteinbasedmodel--fig1.jpeg>
<p>By running our model with the following initial conditions:</p>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/9/91/T--oxford--proteinbasedmodel--fig2.jpeg>
<ul>
+
<p>The simulations of both models show that the single inhibitory system approaches the threshold (t = 10.42 min) much faster than the double inhibitory system (t = 55.89 min). This is as expected as the additional inhibitory coil in the double inhibitory coil system increases the total amount of substrates that the cruzipain has to cleave. Hence, the production of hirudin is much slower in the double inhibitory coil system and but accelerates later on. The single inhibitory coil system has a steady initial production rate and as a result approaches the threshold much faster.</p>
  <li>50 nM of repressed DNA</li>
+
<p>To see how these systems affect the rate of false positives, we ran the simulations with 0 cruzipain. However, there was very little production of hirudin in either system, and is insufficient to inhibit blood coagulation. Hence, modelling showed that we should stay with a single inhibitory coil system.</p>
  <li>50 nM of excess TetR dimer</li>
+
<p>In fact, we tried running it without any feedback loop, and it shows that the direct cleavage of an inactivated form of hirduin by cruzipain actually has the fastest threshold time.</p>
  <li>360 pM of Cruzipain (Positive Test)</li>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/a/a5/T--oxford--proteinbasedmodel--fig5.png>
  <li>30 nM of RNAP</li>
+
  <li>30 nM of Ribosomes</li>
+
</ul>
+
<p>We only used repressed DNA in this simulation, because that will give us the rate of production of hirudin without considering the leakiness of the promoter. In other words, this should give us the slowest production rate in the scenario.</p>
+
<h4 class = "text-center">Results:</h4>
+
<h6>[Fig 1]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/c/ce/T--oxford--DNA_model--fig1.png></img>
+
<p>From the graph above, we see that it takes around 31 minutes to produce the threshold amount of hirudin. This delay is too long for the hirudin to effectively inhibit blood coagulation. In fact, running the blood coagulation model using this output revealed the presence of a thrombin spike at close to 8 minutes, indicating the initiation of blood coagulation. See the blood coagulation model for more details.</p>
+
<h6>[Fig 2]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/8/8f/T--oxford--DNA_model--fig2.png></img>
+
<p>To see if we could increase the hirudin production rate, we tried changing the concentration of DNA.</p>
+
<h6>[Fig 3]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/3/3e/T--oxford--DNA_model--fig3.png></img>
+
<p>We ran the simulations from 10 nM to 100 nM of DNA at 10 nM intervals. We see that increasing the amount of DNA can reduce the time taken to threshold, however, there is saturation at high concentrations of DNA. Further increasing the DNA concentration in our simulations revealed that it is unable to reduce the threshold time to lower than 20 minutes (at 3 μM of DNA), which is still too slow to inhibit blood coagulation.</p>
+
<p>Another initial condition that could be changed is the amount of excess TetR that is not bound to a promoter. To reduce chances of leakage, we assumed that the amount of TetR added to the kit will be higher than the amount of DNA. However, these excess TetR will competitively inhibit cruzipain cleavage of the TetR that is bound to the promoters. This can be seen in the graph below</p>
+
<h6>[Fig 4]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/1/13/T--oxford--DNA_model--fig4.png></img>
+
<h6>[Fig 5]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/b/b7/T--oxford--DNA_model--fig5.png></img>
+
<p>We ran simulations at different concentrations of excess TetR from 0 nM to 50 nM, but found very little difference in the hirudin threshold time.</p>
+
<p>Finally, we ran a sensitivity analysis on the model parameters to determine the most suitable parameters to change to have the best impact on the hirudin production rate.</p>
+
<h6>[Fig 6]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/f/f4/T--oxford--DNA_model--fig6.png></img>
+
<p>The vertical axis represents the different model parameters and the horizontal axis represents the relevant outputs of the model. The first five columns are the end-point (t = 3600s) concentrations of the respective species. The last three columns are metrics representing the dynamics of hirudin production. Initial hirudin Rate is taken to be the average rate over the first 10 seconds, which is useful in knowing the response delay. hirudin Rise Time is the time it takes to reach 90% of steady state (in this case, the end-point), which is a common way of measuring a system’s dynamic. The last is the just the maximum derivative of hirudin concentration.</p>
+
<p>The sensitivity of each parameter to each output is normalized to represent the ratio of the relative change in output to the relative change in the parameter. We see that changing the reaction kinetics of cruzipain will have a large effect on the final concentration of TetR, which is as expected. To find the best parameters to increase hirudin production rate, we see it is sensitive to the parameters k_TX, k_TL, ka_RNAP_DNA and K_Rib. Hence, changing the promoter strength or ribosome binding site (RBS) strength could increase the production rate.</p>
+
<p>However, a better method is to add amplification to our system.</p>
+
  
<h3 class = "text-center">2. Assumptions</h3>
+
<h2 class = "text-center">Stochastic Modelling – Intrinsic Noise</h2>
<p>Due to the slow rate of directly expressing hirudin. We decided to include an amplification step into our system. This is achieved by producing TEV instead, which will then cleave a sterically inactivated form of hirudin to release hirudin.</p>
+
<p>To assess how stochasticity might affect our system, we simulated the protein-based system using the tau-leaping method.</p>
<p>This is modelled by adding the following reactions and replacing the translation product with TEV.</p>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/5/55/T--oxford--proteinbasedmodel--fig3.jpeg>
\(K_{b}\)
+
<p>We plotted the final hirudin amount in a histogram and fitted a normal probability distribution function to the data. Looking at the horizontal scale, we see that the variation in the final amount of Hirudin is very low. In fact, the relative standard distribution is \(4.78*10^{-8}\), indicating that the effects of inherent stochasticity is negligible in our system at a reaction volume of 30 µL.</p>
<p>[Insert HTML]</p>
+
<p>Hence, this justifies our use of deterministic models in simulating both our DNA-based and Protein-based models.</p>
<h6>[Fig 7]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/3/32/T--oxford--DNA_model--fig7.png></img>
+
<p>The graph shows that using an amplification step will greatly increase the speed of the release of hirudin. The threshold time is reduced to 11 minutes from 31 minutes. Even though the production of TEV is slower as TEV has a longer sequence, it was still able to accelerate the release of hirudin.</p>
+
<p>As 11 minutes is very close to the common blood coagulation initiation time of 5-10 minutes, it is hard to tell if the blood will clot or not. Hence, we ran the blood coagulation model with this model’s output. </p>
+
<h6>[Fig 8]</h6>
+
<img class = "img-responsive" src =https://static.igem.org/mediawiki/2017/d/d5/T--oxford--DNA_model--fig8.png></img>
+
<p>The lack of a distinct peak here indicates no initiation of blood coagulation, showing that an amplification step is effective.</p>
+
  
<h3 class = "text-center">Promoter Leakage</h3>
+
<h2>Discussions</h2>
<p>For this set of simulations, we tried to determine the amount of hirudin produced for a negative test (no Cruzipain) and a positive test. As a biosensor and a diagnostic, it is important that there is a large difference between a negative and positive result. Furthermore, it is important that a negative test does not produce enough hirudin to stop blood coagulation.</p>
+
<p>We showed that the protein-based system works and that it's feedback loop indeed accelerates the release of hirudin. However, the coils we have chosen have a high K<sub>d</sub> and that decreases the effectiveness of the feedback loop. As seen, the feedback loop increases the competitive inhibition of Cruzipain substrates, and so a system without feedback loops turned out to have the fastest threshold time.</p>
<p>This time, we assume all the DNA to be initially unrepressed, as that will give the highest leakage. The initial conditions are:</p>
+
<ul>
+
  <li>50 nM of DNA</li>
+
  <li>100 nM of TetR dimer</li>
+
  <li>360 pM of Cruzipain (Positive Test) and 0 pM of Cruzipain (Negative Test)</li>
+
  <li>30 nM of RNAP</li>
+
  <li>30 nM of Ribosomes</li>
+
</ul>
+
<h6>[Fig 9]</h6>
+
<img class = "img-responsive" src =fig9></img>
+
  
 
<h2 class = "text-center">References</h2>
 
<h2 class = "text-center">References</h2>
Line 259: Line 230:
 
     <tr>
 
     <tr>
 
       <td>B</td>
 
       <td>B</td>
       <td>Hillen, W. et al. (1983) ‘Control of expression of the Tn10-encoded tetracycline resistance genes. Equilibrium and kinetic investigation of the regulatory reactions’, Journal of Molecular Biology, 169(3), pp. 707–721. doi: 10.1016/S0022-2836(83)80166-1.</td>
+
       <td>Shekhawat, S.S., Porter, J.R., Sriprasad, A. and Ghosh, I., 2009. An autoinhibited coiled-coil design strategy for split-protein protease sensors. Journal of the American Chemical Society, 131(42), pp.15284-15290.</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
 
       <td>C</td>
 
       <td>C</td>
       <td>Karzbrun, E. et al. (2011) ‘Coarse-grained dynamics of protein synthesis in a cell-free system’, Physical Review Letters, 106(4), pp. 1–4. doi: 10.1103/PhysRevLett.106.048104.</td>
+
       <td>Wehr, M.C., Laage, R., Bolz, U., Fischer, T.M., Grünewald, S., Scheek, S., Bach, A., Nave, K.A. and Rossner, M.J., 2006. Monitoring regulated protein-protein interactions using split TEV. Nature methods, 3(12), pp.985-993.</td>
 
     </tr>
 
     </tr>
 
     <tr>
 
     <tr>
 
       <td>D</td>
 
       <td>D</td>
       <td>Kleinschmidt, C. et al. (1988) ‘Dynamics of Repressor—Operator Recognition: The Tn10-encoded Tetracycline Resistance Control’, Biochemistry, 27(4), pp. 1094–1104. doi: 10.1021/bi00404a003.</td>
+
       <td>Cabrita, L.D., Gilis, D., Robertson, A.L., Dehouck, Y., Rooman, M. and Bottomley, S.P., 2007. Enhancing the stability and solubility of TEV protease using in silico design. Protein science, 16(11), pp.2360-2367.</td>
    </tr>
+
    <tr>
+
      <td>E</td>
+
      <td>Murray, D. N. A. M. G. and Biology, P. (2008) ‘Nucleic Acids Research’, Nucleic Acids Research, 36(19), pp. ii–ii. doi: 10.1093/nar/gkn907.</td>
+
     
+
    </tr>
+
    <tr>
+
      <td>F</td>
+
      <td>Stogbauer, T. R., 2012. Experiment and quantitative modeling of cell free gene expression dynamics, Munich: Ludwig–Maximilians–University.</td>
+
 
     </tr>
 
     </tr>
 
</thead>
 
</thead>

Latest revision as of 03:51, 8 December 2017

Protein-Based System Models

Introduction

We developed a number of models for the protein based system to simulate its dynamics. We used this model to inform us of the effectiveness of using a positive feedback loop, the difference between using a singly-inhibited and doubly-inhibited coil, and also the possibility of using different types of coiled-coils to improve the performance of our system.

When designing this system, we were aware of the potential of more false positives, as the inhibitory coil could unfold without being cleaved by Cruzipain. This allows the two coiled-coils that are now uninhibited to associate and the resulting split TEV protease might activate the system. It is our goal to quantify this effect.

Methodology

The model simulates the cleavage by Cruzipain using Michaelis-Menten kinetics, including the competitive inhibition of multiple identical substrates. The association and dissociation rates of the coiled-coils are estimated from the dissociation constant. The split TEV protease fragments are assumed to have no effect on the association and dissociation of the coiled coils.

The following parameters were used:

Parameter Variable Name Value Reference*
Catalysed rate of reaction for Cruzipain \(k_{cat,c}\) \(10.8\) A
Michaelis Constant for Cruzipain \(K_{m,c}\) \(5.8*10^{-6}\) A
Catalysed rate of reaction for splitTEV \(k_{cat,sTEV}\) \(0.222\) C & D
Michaelis Constant for splitTEV \(K_{m,sTEV}\) \(121*10^{-6}\) C & D
Change in Free Energy of the Dimerisation of A and B \(\Delta G_{A:B}\) \(-6.1\) B
Dissociation Constant of A coil and B coil \(K_{d,A:B}\) \(e^{\frac{\Delta G_{A:B}}{R*T}}=e^{\frac{-6.1}{1.99*10^{-3}*(273+25)}}=0.0038\) B
Dissociation Rate of A coil and B coil \(k_{d,A:B}\) \(K_{d,A:B}*k_{a,A:B}=3.8*10^{-7}\) B
Association Rate of A coil and B coil \(k_{a,A:B}\) \(10^4\) B
Empirical Adjusted Ratio for A:Inhibitory Coil and B Dissociation Constant based on Experimental Data \(R_{A:B^*,B}\) \(32\) B
Empirical Adjusted Ratio for A and A:B Dissociation Constant based on Experimental Data \(R_{A,A:B}\) \(17\) B
Empirical Adjusted Ratio for A:Inhibitory Coil and A:B Dissociation Constant based on Experimental Data \(R_{A:B^*,A:B}\) \(1000\) B

The following species were used

Species Name Symbol Used
Cruzipain \(Cruzipain\)
N Terminal SplitTEV A B* \(V300\)
N Terminal SplitTEV A B* with B* cleaved \(V300_{cleaved}\)
C Terminal SplitTEV B \(V400\)
V200 anchored OMV (sterically-hindered) \(V200_{OMV}\)
V300 anchored OMV (sterically-hindered) \(V300_{OMV}\)
V400 anchored OMV (sterically-hindered) \(V400_{OMV}\)
Hirudin \(Hirudin\)
SplitTEV with the inhibitory coil is cleaved (V300_cleaved + V400) i \(splitTEV_{clean}\)
SplitTEV with unfolded inhibitory coil (V300 + V400) \(splitTEV_{coil}\)
The sume of splitTEV_clean + splitTEV_coil \(splitTEV\)
V200_OMV + V300_OMV + V400_OMV + V300 \(substrates_{cruzipain}\)
V200_OMV + V300_OMV + V400_OMV + V300 \(substrates_{splitTEV}\)

The following reactions were modelled for single inhibitory coil:

Cruzipain Cleavage Reactions

$$ 1. V200_{OMV} + Cruzipain \leftrightharpoons V200_{OMV}:Cruzipain \to Hirudin + Cruzipain $$ $$ 2. V300_{OMV} + Cruzipain \leftrightharpoons V300_{OMV}:Cruzipain \to V300 + Cruzipain $$ $$ 3. V400_{OMV} + Cruzipain \leftrightharpoons V400_{OMV}:Cruzipain \to V400 + Cruzipain $$ $$ 4. V300 + Cruzipain \leftrightharpoons V300:Cruzipain \to V300_{cleaved} + Cruzipain $$

splitTEV Cleavage Reactions

$$ 1. V200_{OMV} + splitTEV \leftrightharpoons V200_{OMV}:splitTEV \to Hirudin + splitTEV$$ $$ 2. V300_{OMV} + splitTEV \leftrightharpoons V300_{OMV}:splitTEV \to V300 + splitTEV$$ $$ 3. V400_{OMV} + splitTEV \leftrightharpoons V400_{OMV}:splitTEV \to V400 + splitTEV $$ $$ 4. V300 + splitTEV \leftrightharpoons V300:splitTEV \to V300_{cleaved} + splitTEV$$

Coiled-Coil Interactions

$$ 1. V300_{cleaved} + V400 \leftrightharpoons splitTEV_{clean}$$ $$ 2. V300 + V400 \leftrightharpoons splitTEV_{coil} $$

The following reactions were modelled for double inhibitory coil:

Cruzipain Cleavage Reactions

$$ 1. V200_{OMV} + Cruzipain \leftrightharpoons V200_{OMV}:Cruzipain \to Hirudin + Cruzipain $$ $$ 2. V300_{OMV} + Cruzipain \leftrightharpoons V300_{OMV}:Cruzipain \to V300 + Cruzipain $$ $$ 3. V400_{OMV} + Cruzipain \leftrightharpoons V400_{OMV}:Cruzipain \to V400 + Cruzipain $$ $$ 4. V300 + Cruzipain \leftrightharpoons V300:Cruzipain \to V300_{cleaved} + Cruzipain $$

splitTEV Cleavage Reactions

$$ 1. V200_{OMV} + splitTEV \leftrightharpoons V200_{OMV}:splitTEV \to Hirudin + splitTEV$$ $$ 2. V300_{OMV} + splitTEV \leftrightharpoons V300_{OMV}:splitTEV \to V300 + splitTEV$$ $$ 3. V400_{OMV} + splitTEV \leftrightharpoons V400_{OMV}:splitTEV \to V400 + splitTEV $$ $$ 4. V300 + splitTEV \leftrightharpoons V300:splitTEV \to V300_{cleaved} + splitTEV$$ $$ 5. V400 + splitTEV \leftrightharpoons V300:splitTEV \to V400_{cleaved} + splitTEV$$

Coiled-Coil Interactions

$$ 1. V300_{cleaved} + V400 \leftrightharpoons splitTEV_{v400coil}$$ $$ 2. V300 + V400 \leftrightharpoons splitTEV_{bothcoils} $$ $$ 3. V300 + V400_{cleaved} \leftrightharpoons splitTEV_{v300coil}$$ $$ 4. V300_{cleaved} + V400_{cleaved} \leftrightharpoons splitTEV_{clean} $$

These reactions were modelled in ODEs and simulated using the ode15s function at an absolute tolerance of 10-30 and a relative tolerance of 10-7.

Single Inhibitory Coil or Double Inhibitory Coil

We wanted to know if using a single inhibitory coil system or a double inhibitory coil system would give the best result. The main benefit of a double inhibitory coil system is that it is less likely for both inhibitory coil to unfold and cause the coiled-coils to dimerise. As shown by Shekhawat et al., a doubly inhibited coiled-coil has a 1040 fold-difference in output in the absence and presence of a TEV protease, whereas a single inhibited coiled-coil only has a 22 fold-difference. Hence, we wanted to see how a double inhibitory coil system would affect the performance of a system.

The simulations of both models show that the single inhibitory system approaches the threshold (t = 10.42 min) much faster than the double inhibitory system (t = 55.89 min). This is as expected as the additional inhibitory coil in the double inhibitory coil system increases the total amount of substrates that the cruzipain has to cleave. Hence, the production of hirudin is much slower in the double inhibitory coil system and but accelerates later on. The single inhibitory coil system has a steady initial production rate and as a result approaches the threshold much faster.

To see how these systems affect the rate of false positives, we ran the simulations with 0 cruzipain. However, there was very little production of hirudin in either system, and is insufficient to inhibit blood coagulation. Hence, modelling showed that we should stay with a single inhibitory coil system.

In fact, we tried running it without any feedback loop, and it shows that the direct cleavage of an inactivated form of hirduin by cruzipain actually has the fastest threshold time.

Stochastic Modelling – Intrinsic Noise

To assess how stochasticity might affect our system, we simulated the protein-based system using the tau-leaping method.

We plotted the final hirudin amount in a histogram and fitted a normal probability distribution function to the data. Looking at the horizontal scale, we see that the variation in the final amount of Hirudin is very low. In fact, the relative standard distribution is \(4.78*10^{-8}\), indicating that the effects of inherent stochasticity is negligible in our system at a reaction volume of 30 µL.

Hence, this justifies our use of deterministic models in simulating both our DNA-based and Protein-based models.

Discussions

We showed that the protein-based system works and that it's feedback loop indeed accelerates the release of hirudin. However, the coils we have chosen have a high Kd and that decreases the effectiveness of the feedback loop. As seen, the feedback loop increases the competitive inhibition of Cruzipain substrates, and so a system without feedback loops turned out to have the fastest threshold time.

References

Index Reference
A Dos Reis, F. C. G. et al. (2006) ‘The substrate specificity of cruzipain 2, a cysteine protease isoform from Trypanosoma cruzi’, FEMS Microbiology Letters, 259(2), pp. 215–220. doi: 10.1111/j.1574-6968.2006.00267.x.
B Shekhawat, S.S., Porter, J.R., Sriprasad, A. and Ghosh, I., 2009. An autoinhibited coiled-coil design strategy for split-protein protease sensors. Journal of the American Chemical Society, 131(42), pp.15284-15290.
C Wehr, M.C., Laage, R., Bolz, U., Fischer, T.M., Grünewald, S., Scheek, S., Bach, A., Nave, K.A. and Rossner, M.J., 2006. Monitoring regulated protein-protein interactions using split TEV. Nature methods, 3(12), pp.985-993.
D Cabrita, L.D., Gilis, D., Robertson, A.L., Dehouck, Y., Rooman, M. and Bottomley, S.P., 2007. Enhancing the stability and solubility of TEV protease using in silico design. Protein science, 16(11), pp.2360-2367.