Difference between revisions of "Team:TU Darmstadt/Model"

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                 <a href="#Chapter 1">EEEWhy Modelling</a>
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                 <a href="#Chapter 1">Introduction</a>
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                 <a href="#Chapter 2">Modelling and Corona</a>
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                <a href="#Chapter 3">Biofilm Simulation</a>
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In synthetic biology, <b>modeling </b> is a powerful tool that uses theoretical models and <b> computational approaches </b> to predict, improve and further understand experiments. This year, modeling turned out to be one of the crucial cornerstones of our project due to the cancelled lab time. </div>
 
In synthetic biology, <b>modeling </b> is a powerful tool that uses theoretical models and <b> computational approaches </b> to predict, improve and further understand experiments. This year, modeling turned out to be one of the crucial cornerstones of our project due to the cancelled lab time. </div>
 
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Over the course of the project, it became increasingly clear that we wouldn’t be able to test out our project in the <b> laboratory </b>, which meant for us to adapt to the current situation and focus on different aspects of our project such as the modeling part. In this context, we built a total of three distinct models to gain further insights into the underlying biochemistry of our wastewater treatment approach:</div>
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Over the course of the project, it became increasingly clear that we wouldn’t be able to test out our project in the <b> laboratory</b>, which meant for us to adapt to the current situation and focus on different aspects of our project such as the modeling part. In this context, we built a total of three distinct models to gain further insights into the underlying biochemistry of our wastewater treatment approach:</div>
 
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We used the <b> Rosetta Commons Software </b> [1] developed by Baker Lab as well as the computational power of the Lichtenberg high-performance-computer at the TU Darmstadt to predict <b> enzyme structures </b>, test their stability, predict <b> enzyme-ligand-interactions </b> and enhance those by modifying the enzyme’s <b> active sites </b>. We wrote a python program based on the work of B. Qin et al [2] to simulate the growth mechanics and -conditions of our <b> biofilm </b> in cooperation with the modeling team from iGEM Hannover as well as an ODE-based MATLAB-model [3] to conceptually represent the functionality of our <b> kill switch </b>.</div>  
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We used the <b> Rosetta Commons Software </b> developed by Baker Lab as well as the computational power of the Lichtenberg high-performance-computer at the TU Darmstadt to predict <b>enzyme structures</b>, test their stability and predict <b> enzyme-ligand-interactions</b><sup id="cite_ref-1"><a href="#cite_note-1">[1]</a></sup>.We wrote a python program based on the work of <i>B. Qin et al.</i> to simulate the growth mechanics and -conditions of our <b> biofilm </b> in cooperation with the modeling team from iGEM Hannover as well as an ODE-based MATLAB-model to conceptually represent the functionality of our <b> kill switch</b><sup id="cite_ref-2"><a href="#cite_note-2">[2,</a></sup> <sup id="cite_ref-3"><a href="#cite_note-3">3]</a></sup>.</div>  
 
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Since we weren’t able to include any self-generated data into our models this year, we hope that <b> future iGEM teams </b> can be inspired to use and fill them with life by expanding on them and implementing their own parameters. Our biofilm model can be used to describe biofilms built by other bacteria than <i>B. subtilis</i> simply by utilizing the values that describe those best. This also holds true for the kill switch model, which can be adapted to different biofilm-related systems if given the right data. </div>
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Since we weren’t able to include any self-generated data into our models this year, we hope that <b> future iGEM teams </b> can be inspired to use and fill them with life by expanding on them and implementing their own parameters. Our biofilm model can be used to describe biofilms built by other bacteria than <i>B.&nbsp;subtilis</i> simply by utilizing the values that describe those best. This also holds true for the kill switch model, which can be adapted to different biofilm-related systems if given the right data.<br>
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To make it short, through our modeling work we were able to predict that the most important aspects of our project which are listed below will work as intended and we layed the ground work for further optimizations.    </div>
 
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The described models can be further examined on the following pages.
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                          <b>The described models can be further examined on the following pages:</b>
 
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                 <a href="https://2020.igem.org/Team:TU_Darmstadt/Model/Kill_Switch_Modeling">
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Click the monitors to get to different parts of our modeling.
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        Why Rosetta?
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        <i>Rosetta</i> is a software suite capable of <b>solving a multitude of computational macromolecular problems</b> such as de novo protein design, enzyme design,
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        ligand docking and structure prediction of biological macromolecules or macromolecular complexes. The Rosetta energy functions enables <b>relatively precise</b>
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        solution of a broad range of applications by considering many different energy terms relevant for protein folding such as solvation, electrostatic effects and hydrogen bonding.
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        It can be used to perform simulations starting from <b>designing macromolecular structures, interactions and RNA or fibril structures up to the de novo design of a fully functioning enzyme</b>!
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        It was originally developed by the David Baker Lab. <sup id="cite_ref-1"><a href="#cite_note-1">[1]</a></sup> Rosetta is <b>free for academic users</b> and a very powerful tool for multiple problems that come along with elaborating a synthetic biology problem.
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        Although there are lot of information available in the Rosetta Documentation, it is very hard for people that want to get started with Rosetta to improve their project, especially for people without experience with console-based applications.
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        Nevertheless, Rosetta displays an <b>amazing and multifaceted tool for synthetic biology</b>. To counter the starting issues with the program we provide a <b>guide for Rosetta</b> on our Wiki. <br><br>
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        We used multiple Rosetta applications to model the properties of our enzymes. The collected data allows us to predict enzyme functionality when immobilized in our biofilm with TasA,
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        binding affinity towards different pharmaceuticals or pollutants and many more.<br>
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        <b> RosettaCM</b> was used to generate <b>structure predictions</b> of the azithromycin transforming enzyme <b>EreB</b> and <b> fusion proteins</b> consisting of matrix protein <b> TasA</b> and our enzymes <b>CueO, CotA and EreB</b> <sup id="cite_ref-2"><a href="#cite_note-2">[2]</a></sup>.<br>
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        <b> Rosetta Ligand Docking</b> was used to <b>study the binding affinity</b> of various substrates towards the enzymes active site<sup id="cite_ref-3"><a href="#cite_note-3">[3]</a></sup>.<br>
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        <b> Protein Design</b> was used to<b> enhance the binding affinity</b> of our target molecules to the corresponding enzymes’ active site by introduction of mutations <sup id="cite_ref-4"><a href="#cite_note-4">[4]</a></sup>.<br><br>
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        To find out more about the different aspects of the modelling using Rosetta you can read the articles thematizing these applications. If you want to use Rosetta on your own you can use our <a href = “https://2020.igem.org/Team:TU_Darmstadt/Model/Rosetta_Guide“>Rosetta Guide</a> to get started with the program.  
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                 <h2 style="font-family: 'Exo 2', sans-serif; font-weight: 700"> Achievements </h2>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We defined how diclofenac most likely <b>binds to the laccase CotA</b><br>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We were able to define a starting point for <b>enzyme optimization</b><br>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We predicted a structure for the <b>esterase EreB</b><br>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We predicted structures for the TasA-CotA as well as TasA-EreB <b>fusion proteins</b> and proved their stability<br>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We provided a software tool for the prediction of growth, density and stability of <b>biofilms</b><br>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We simulated the <b>growth kinetics</b> of a <i>B.&nbsp;subtilis</i> biofilm<br>
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<img src="https://static.igem.org/mediawiki/2020/0/04/T--TU_Darmstadt--Checkbox_2020.svg" width="3%" height="auto"> We created a model for the simulation of the <b><i>comXQPA</i> system</b>, which can be used for the optimization for our kill switch<br>
  
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            <a class="referencestd" href="https://www.rosettacommons.org/" target="_blank">[1] https://www.rosettacommons.org/ (accessed on October 22, 2020)  </a>
  
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            <a class="referencestd" href="https://science.sciencemag.org/content/369/6499/71.abstract" target="_blank">[2] Cell position fates and collective fountain flow in bacterial biofilms revealed by light-sheet microscopy, B. Qin et al., Science 10.1126/science.abb8501, 2020</a>
  
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            <a class="referencestd" href="https://www.mathworks.com/products/simbiology.html" target="_blank">[3] MATLAB and SimBiology Toolbox  2020a, The MathWorks, Inc., Natick, Massachusetts, United States. </a>
  
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        <a  class="anchor" id="cite_note-1"></a><a class="referencestd" href=" https://doi.org/10.1016/S0076-6879(04)83004-0" target="_blank">1.  CA. Roth et al. Protein Structure Prediction Using Rosetta. Methods in Enzymology 2004, 382 66-93, https://doi.org/10.1016/S0076-6879(04)83004-0 </a>
 
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        <a  class="anchor" id="cite_note-2"></a>  <a class="referencestd" href="https://doi.org/10.1016/j.str.2013.08.005" target="_blank">3. Song Y et al. High-Resolution Comparative Modeling with RosettaCM. Structure 2013, 21 1735-1742, https://doi.org/10.1016/j.str.2013.08.005</a>
 
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        <a  class="anchor" id="cite_note-3"></a><a class="referencestd" href=" https://doi.org/10.1038/nprot.2013.074" target="_blank">1.  SA. Combs et al. Small-molecule ligand docking into comparative models with Rosetta, Nature Protocol 2013, 8(7) 1277-98, doi: 10.1038/nprot.2013.074</a>
 
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        <a class="referencestd" href="https://doi.org/10.1007/978-1-4939-3569-7_4" target="_blank">7. Moretti R et al. Rosetta and the Design of Ligand Binding Sites. Methods Mol Biol. 2016; 1414: 47–62. doi: 10.1007/978-1-4939-3569-7_4</a>
 
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Latest revision as of 16:13, 26 October 2020

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Introduction Achievements
Introduction

Introduction

In synthetic biology, modeling is a powerful tool that uses theoretical models and computational approaches to predict, improve and further understand experiments. This year, modeling turned out to be one of the crucial cornerstones of our project due to the cancelled lab time.
Over the course of the project, it became increasingly clear that we wouldn’t be able to test out our project in the laboratory, which meant for us to adapt to the current situation and focus on different aspects of our project such as the modeling part. In this context, we built a total of three distinct models to gain further insights into the underlying biochemistry of our wastewater treatment approach:
We used the Rosetta Commons Software developed by Baker Lab as well as the computational power of the Lichtenberg high-performance-computer at the TU Darmstadt to predict enzyme structures, test their stability and predict enzyme-ligand-interactions[1].We wrote a python program based on the work of B. Qin et al. to simulate the growth mechanics and -conditions of our biofilm in cooperation with the modeling team from iGEM Hannover as well as an ODE-based MATLAB-model to conceptually represent the functionality of our kill switch[2, 3].
Since we weren’t able to include any self-generated data into our models this year, we hope that future iGEM teams can be inspired to use and fill them with life by expanding on them and implementing their own parameters. Our biofilm model can be used to describe biofilms built by other bacteria than B. subtilis simply by utilizing the values that describe those best. This also holds true for the kill switch model, which can be adapted to different biofilm-related systems if given the right data.
To make it short, through our modeling work we were able to predict that the most important aspects of our project which are listed below will work as intended and we layed the ground work for further optimizations.
The described models can be further examined on the following pages:


Click the monitors to get to different parts of our modeling.
Achievements

Achievements

We defined how diclofenac most likely binds to the laccase CotA
We were able to define a starting point for enzyme optimization
We predicted a structure for the esterase EreB
We predicted structures for the TasA-CotA as well as TasA-EreB fusion proteins and proved their stability
We provided a software tool for the prediction of growth, density and stability of biofilms
We simulated the growth kinetics of a B. subtilis biofilm
We created a model for the simulation of the comXQPA system, which can be used for the optimization for our kill switch


References

[1] https://www.rosettacommons.org/ (accessed on October 22, 2020) [2] Cell position fates and collective fountain flow in bacterial biofilms revealed by light-sheet microscopy, B. Qin et al., Science 10.1126/science.abb8501, 2020 [3] MATLAB and SimBiology Toolbox 2020a, The MathWorks, Inc., Natick, Massachusetts, United States.