Difference between revisions of "Team:US AFRL CarrollHS/Model"

 
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      <img src="https://static.igem.org/mediawiki/2018/5/5b/T--US_AFRL_CarrollHS--ModelHeader.jpg" alt="Model">
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<div class="row"><h1>Overview</h1></div>
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<div class="row"><p>The goal of the modeling for our project is to determine what ribosomal binding site (RBS) provides the optimal amount of CheZ expression, as too much CheZ can negatively impact the microbes chemotactic ability, but a high enough concentration is required to initiate chemotaxis. Although the modeling below uses arbitrary units and therefore only useful for determining relative amounts of CheZ produced, it provides a good idea of which RBS’s result in much too little protein expression.  Additionally, in the future, we can measure the protein expression obtained with one of the RBS’s and then use the modeling to predict the absolute production that each of the other RBS’s would yield.</p></div>
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<img src="https://static.igem.org/mediawiki/2018/e/e1/T--US_AFRL_CarrollHS--_WhiteNavyDNA.png" style="width: 100%;">
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<div class="row"><h1>Method</h1></div>
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<div class="row">
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<p>We modeled the effect of RBS strength on expression of CheZ, the protein that causes our engineered microbe to move in a straight line. We used COPASI software for our modelling, and the differential equations we used are shown below.  Data for relative RBS strength came from the <a href="http://parts.igem.org/Ribosome_Binding_Sites/Prokaryotic/Constitutive/Community_Collection" style="color: white;">iGEM registry</a>. The RBS strength was changed in the model by changing k1R3.
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</p>  </div>
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<div class="row"><h2>Reactions in Model
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</h2></div>
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<div class="row"><p>R1)  (Transcription) DNA -> mRNA + DNA<br>
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R2)  (Degradation of mRNA) mRNA -> mRNA0<br>
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R3)  (Ribosome binding to mRNA) mRNA + ribo = mRNA_ribo<br>
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R4)  (Translation) mRNA_ribo -> peptide + mRNA_ribo<br>
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R5)  (Degradation of peptides) peptide -> peptide0<br>
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R6)  (Maturation) peptide -> protein<br>
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R7)  (Degradation of proteins) protein -> protein0
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</p></div>
  
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<div class="row"><h2>Kinetic Constants </h2></div>
  
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<h3>★  ALERT! </h3>
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<p>Km - Michaelis-Menten constant<br>
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<p>This page is used by the judges to evaluate your team for the <a href="https://2018.igem.org/Judging/Medals">medal criterion</a> or <a href="https://2018.igem.org/Judging/Awards"> award listed below</a>. </p>
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VR1 - Max rate of reaction for reaction 1<br>
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<p> Delete this box in order to be evaluated for this medal criterion and/or award. See more information at <a href="https://2018.igem.org/Judging/Pages_for_Awards"> Instructions for Pages for awards</a>.</p>
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k1RN - Rate constant for the forward reaction N<br>
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k2RN - Rate constant for the reverse of reaction N<br>
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Vc1 - Volume of the compartment </p>
 
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<img src="https://static.igem.org/mediawiki/2018/c/c4/T--US_AFRL_CarrollHS--ModelingEquations.jpg" style="width: 60%; margin-left:20%; margin-right: 20%;">
  
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<div class="row"><p>Equation (1) models transcription and uses Michaelis-Menten kinetics.  The Law of Mass Action was used for Equations (2), (3), (5), and (6), which describe the rate of change of the concentrations of the peptide, protein, mRNA-ribosome complex, and ribosome respectively. Equations (4), (7), and (8) are also based on the Law of Mass Action and account for the degradation of mRNA, peptides, and the proteins respectively.  The initial concentrations of DNA and ribosomes were set at 1, and all other initial concentrations were 0.
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<h1> Modeling</h1>
 
  
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
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<img src="https://static.igem.org/mediawiki/2018/6/6a/T--US_AFRL_CarrollHS--_NavyWhiteDNA.png" style="width: 100%;">
  
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<div class="row"><h1>Results and Discussion</h1></div>
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<!--<table class="table">
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  <thead>
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    <tr>
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      <th scope="col">RBS</th>
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      <th scope="col">Relative Strength</th>
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      <th scope="row">RBS 29</th>
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      <td>0.764</td>
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      <th scope="row">RBS 32</th>
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      <td>0.376</td>
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      <th scope="row">RBS 33</th>
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      <td>0.002</td>
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      <th scope="row">RBS 34</th>
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      <td>1.000</td>
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      <th scope="row">RBS 35</th>
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      <td>1.124</td>
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  <div class="col-md-6 text-center"><img src="https://static.igem.org/mediawiki/2018/4/48/T--US_AFRL_CarrollHS--RBSData1A.png" width="70%"></div>
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  <div class="col-md-6 text-center"><img src="https://static.igem.org/mediawiki/2018/2/22/T--US_AFRL_CarrollHS--RBSData2A.png" width="70%"></div>
 
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<img src="https://static.igem.org/mediawiki/2018/1/1f/T--US_AFRL_CarrollHS--RBSGraph1.png" style="width: 60%; margin-left:20%; margin-right: 20%;"><br>
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<h3> Gold Medal Criterion #3</h3>
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<p>
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Convince the judges that your project's design and/or implementation is based on insight you have gained from modeling. This could be either a new model you develop or the implementation of a model from a previous team. You must thoroughly document your model's contribution to your project on your team's wiki, including assumptions, relevant data, model results, and a clear explanation of your model that anyone can understand.  
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The model should impact your project design in a meaningful way. Modeling may include, but is not limited to, deterministic, exploratory, molecular dynamic, and stochastic models. Teams may also explore the physical modeling of a single component within a system or utilize mathematical modeling for predicting function of a more complex device.
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Please see the <a href="https://2018.igem.org/Judging/Medals"> 2018
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<div class="col-sm-12 label"><p class="text-center">Fig 1. Levels of CheZ expression for RBS 30,31, 32, 33, 34, and 64 using Data Set 1</p></div>
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Medals Page</a> for more information.  
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<h3>Best Model Special Prize</h3>
 
  
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<img src="https://static.igem.org/mediawiki/2018/4/4c/T--US_AFRL_CarrollHS--ModelingGraph.jpg" style="width: 60%; margin-left:20%; margin-right: 20%;"><br>
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To compete for the <a href="https://2018.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2018.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.
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You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
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</p>
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<div class="row">
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<div class="col-sm-12 label"><p class="text-center">Fig 2. Levels of CheZ expression for RBS 29, 32, 33, 34, and 35 using Data Set 2</p></div>
 
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<div class="row"><p>The results from the first data set indicate that RBS 33 provides very little expression of CheZ, and is most likely not suitable for our purposes.  RBS 30, 31, 32, and 64 all provide relatively high amount of expression, with RBS 34 resulting in the greatest expression of CheZ. The amount of CheZ expressed when using RBS 31 falls between the two extremes.
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<h3> Inspiration </h3>
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Here are a few examples from previous teams:
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</p>
 
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<ul>
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<li><a href="https://2016.igem.org/Team:Manchester/Model">2016 Manchester</a></li>
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<p>The second data set, like the first, indicates that RBS 32 and 34 yield relatively large expression of CheZ. RBS 34 and 35 (34 is covered by 35 in the graph) result in the greatest production of CheZ of the five RBS’s in the set, and RBS 32 and 29 yield slightly less CheZ than RBS 34 and 35. Relative to RBS 34, RBS 33 provides an even lower level of expression in Data Set 2 than in Data Set 1.  </p>
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">2016 TU Delft</li>
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">2014 ETH Zurich</a></li>
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<p>Since the data from the modeling can only be used to determine relative amounts of CheZ expression, we cannot determine with certainty which RBS would provide the optimum level of CheZ expression for our engineered microbe. However, as RBS 33 provided very low levels of expression in both data sets, it is unlikely RBS 33 would be suitable. Furthermore, RBS 31, which was the one used in our project, does not provide an extremely high or low level of CheZ, making it a likely candidate as a viable RBS for our project.</p>
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">2014 Waterloo</a></li>
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</ul>
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<p>While the modelling by itself does not give any information concerning the absolute levels of CheZ expression obtained, experimentally determining the amount of CheZ produced when one of the RBS’s is used would allow us to use the model to make predictions concerning the absolute levels of CheZ expression for the other RBS’s as well.  This would be particularly convenient with RBS 31, as we have already created a construct with RBS 31 and would only have to measure the amount of CheZ produced.</p>
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  </div>
 
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<img src="https://static.igem.org/mediawiki/2018/e/e1/T--US_AFRL_CarrollHS--_WhiteNavyDNA.png" style="width: 100%;">
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<div class="background2">
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<div class="row"><h1>Modeling Collaboration and Attributions</h1></div>
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<div class="row"><h2>NUS-iGEM</h2></div>
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<div class="row"><p>Last year, NUS-iGEM had modeled our project for us, so we began by asking them about their methods. We spoke with Russell and Marcus of the NUS-iGEM about their process for modeling and they gave some tips for modeling our own. They gave us some of their techniques for creating a model using experimental data and spoke about the applications about our project. When modeling our project, we referenced their <a href="https://2017.igem.org/Team:NUS_Singapore/Methodology">parameters and equations</a> from their project last year.</p></div>
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<div class="row"><h2>George Smith</h2></div>
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<div class="row"><p>We next spoke to George Smith, a member of the 2017 CLSB-UK iGEM team. He provided feedback on our proposed modeling ideas, outlined how he used known parameters to model, and explained some of his methods.</p></div>
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<div class="row"><h2>Dr. Yaroslav Chushak</h2></div>
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<div class="row"><p>One of our mentors pointed us towards Dr. Chushak, the modeler in one of our labs. We were able to meet with him several times so he could explain the process of how to model our project. He guided us to help us find the parameters we needed, along with walking us through his process in detail and providing feedback in detail. He also reviewed and provided feedback on our modeling write up.</p></div>
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{{US_AFRL_CarrollHS/footer}}

Latest revision as of 02:41, 18 October 2018

Model

Overview

The goal of the modeling for our project is to determine what ribosomal binding site (RBS) provides the optimal amount of CheZ expression, as too much CheZ can negatively impact the microbes chemotactic ability, but a high enough concentration is required to initiate chemotaxis. Although the modeling below uses arbitrary units and therefore only useful for determining relative amounts of CheZ produced, it provides a good idea of which RBS’s result in much too little protein expression. Additionally, in the future, we can measure the protein expression obtained with one of the RBS’s and then use the modeling to predict the absolute production that each of the other RBS’s would yield.

Method

We modeled the effect of RBS strength on expression of CheZ, the protein that causes our engineered microbe to move in a straight line. We used COPASI software for our modelling, and the differential equations we used are shown below. Data for relative RBS strength came from the iGEM registry. The RBS strength was changed in the model by changing k1R3.

Reactions in Model

R1) (Transcription) DNA -> mRNA + DNA
R2) (Degradation of mRNA) mRNA -> mRNA0
R3) (Ribosome binding to mRNA) mRNA + ribo = mRNA_ribo
R4) (Translation) mRNA_ribo -> peptide + mRNA_ribo
R5) (Degradation of peptides) peptide -> peptide0
R6) (Maturation) peptide -> protein
R7) (Degradation of proteins) protein -> protein0

Kinetic Constants

Km - Michaelis-Menten constant
VR1 - Max rate of reaction for reaction 1
k1RN - Rate constant for the forward reaction N
k2RN - Rate constant for the reverse of reaction N
Vc1 - Volume of the compartment

Equation (1) models transcription and uses Michaelis-Menten kinetics. The Law of Mass Action was used for Equations (2), (3), (5), and (6), which describe the rate of change of the concentrations of the peptide, protein, mRNA-ribosome complex, and ribosome respectively. Equations (4), (7), and (8) are also based on the Law of Mass Action and account for the degradation of mRNA, peptides, and the proteins respectively. The initial concentrations of DNA and ribosomes were set at 1, and all other initial concentrations were 0.

Results and Discussion



Fig 1. Levels of CheZ expression for RBS 30,31, 32, 33, 34, and 64 using Data Set 1




Fig 2. Levels of CheZ expression for RBS 29, 32, 33, 34, and 35 using Data Set 2


The results from the first data set indicate that RBS 33 provides very little expression of CheZ, and is most likely not suitable for our purposes. RBS 30, 31, 32, and 64 all provide relatively high amount of expression, with RBS 34 resulting in the greatest expression of CheZ. The amount of CheZ expressed when using RBS 31 falls between the two extremes.

The second data set, like the first, indicates that RBS 32 and 34 yield relatively large expression of CheZ. RBS 34 and 35 (34 is covered by 35 in the graph) result in the greatest production of CheZ of the five RBS’s in the set, and RBS 32 and 29 yield slightly less CheZ than RBS 34 and 35. Relative to RBS 34, RBS 33 provides an even lower level of expression in Data Set 2 than in Data Set 1.

Since the data from the modeling can only be used to determine relative amounts of CheZ expression, we cannot determine with certainty which RBS would provide the optimum level of CheZ expression for our engineered microbe. However, as RBS 33 provided very low levels of expression in both data sets, it is unlikely RBS 33 would be suitable. Furthermore, RBS 31, which was the one used in our project, does not provide an extremely high or low level of CheZ, making it a likely candidate as a viable RBS for our project.

While the modelling by itself does not give any information concerning the absolute levels of CheZ expression obtained, experimentally determining the amount of CheZ produced when one of the RBS’s is used would allow us to use the model to make predictions concerning the absolute levels of CheZ expression for the other RBS’s as well. This would be particularly convenient with RBS 31, as we have already created a construct with RBS 31 and would only have to measure the amount of CheZ produced.

Modeling Collaboration and Attributions

NUS-iGEM

Last year, NUS-iGEM had modeled our project for us, so we began by asking them about their methods. We spoke with Russell and Marcus of the NUS-iGEM about their process for modeling and they gave some tips for modeling our own. They gave us some of their techniques for creating a model using experimental data and spoke about the applications about our project. When modeling our project, we referenced their parameters and equations from their project last year.

George Smith

We next spoke to George Smith, a member of the 2017 CLSB-UK iGEM team. He provided feedback on our proposed modeling ideas, outlined how he used known parameters to model, and explained some of his methods.

Dr. Yaroslav Chushak

One of our mentors pointed us towards Dr. Chushak, the modeler in one of our labs. We were able to meet with him several times so he could explain the process of how to model our project. He guided us to help us find the parameters we needed, along with walking us through his process in detail and providing feedback in detail. He also reviewed and provided feedback on our modeling write up.

Contact us @iGEMLabPats or iGEM@carrollhs.org