Team:Evry/Metabolism model

From 2013.igem.org

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<h1>Flux model</h1>
<h2>Introduction</h2>
<h2>Introduction</h2>
<p>
<p>
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In this part of our modeling work we focus on genome scale analysis of the enterobactin production pathway. A major concern about our system is its non-orthogonality with the natural metabolic network of the cells : <i>E. Coli</i> already possesses the genes for producing enterobactins. Therefore we wanted to assess the possible interactions between our system and the bacterial metabolism.
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In this part of our modeling work we focus on genome scale analysis of the enterobactin production pathway. A major concern about our system is its non-orthogonality with the natural metabolic network of the cells : <i>E. coli</i> already possesses the genes for producing enterobactins. Therefore we wanted to assess the possible interactions between our system and the bacterial metabolism.
</p>
</p>
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</ul>
</ul>
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The <a href=#Fig1>Figure 1</a> presents the biochemical pathway for producing the enterobactin compound in <i>E. Coli</i>.
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The <a href="#Fig1">Figure 1</a> presents the biochemical pathway for producing the enterobactin compound in <i>E. coli</i>.
</p>
</p>
<div class="captionedPicture" style="float:left;">
<div class="captionedPicture" style="float:left;">
<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/3/3b/EntBS.png">
<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/3/3b/EntBS.png">
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/3/3b/EntBS.png" width="750px" height="381px" alt="enterobactin production pathway" class="Picture"/>
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/3/3b/EntBS.png" width="800px" height="400px" alt="enterobactin production pathway" class="Picture"/>
</a>
</a>
<div class="caption">
<div class="caption">
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<p>
<p>
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As can be seen in the <a href="#Fig1>Figure 1</a> the pathway is 4 steps long and composed of six different enzymes. Hence there exists 4 possible metabolites which concentration may be limiting :
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As can be seen in the <a href="#Fig1">Figure 1</a> the pathway is 4 steps long and composed of six different enzymes. Hence there exists 4 possible metabolites which concentration may be limiting :
<ul>
<ul>
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<p>
<p>
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We highlighted two main kind of interactions between the bacterial and our system :
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We highlighted two main kind of interactions between the bacteria and our system :
<ol>
<ol>
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<li>The synthetic system interacts with the bacterial metabolism. Leading to scarcity of the metabolites involved in the pathway for the other (possibly essential) metabolic reactions of the cell.</li>
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<li>The synthetic system interacts with the bacterial metabolism. Leading to a depletion of the metabolites involved in the pathway for the other (possibly essential) metabolic reactions of the cell.</li>
<li>The other way round, the metabolic reactions could prevent our synthetic system to work as expected by limiting the quantity of the involved metabolites available.</li>
<li>The other way round, the metabolic reactions could prevent our synthetic system to work as expected by limiting the quantity of the involved metabolites available.</li>
</ol>
</ol>
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<ul>
<ul>
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<li><em>Is the metabolic model of E.coli able to provide any information about the possible interactions between our system and the metabolism?</em></li>
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<li><em>Is the metabolic model of <i>E. coli</i> able to provide any information about the possible interactions between our system and the metabolism?</em></li>
<li><em>Is the concentration of any metabolite limiting ?</em></li>
<li><em>Is the concentration of any metabolite limiting ?</em></li>
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<li><em>In the latter case, what is the quantity of this metabolite to add?</em></li>
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<li><em>In the latter case, what is the quantity of precursor that should be added in the medium?</em></li>
</ul>
</ul>
</p>
</p>
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<p>
<p>
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We used the metabolic model <i>E. Coli</i> iJR904 downloaded from the <a href="http://bigg.ucsd.edu/bigg/home.pl">BiGG model database</a><a href="#ref1">[1]</a>. We chose this model because it contains all the metabolites involved in the enterobactin production pathway.
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We used the metabolic model <i>E. coli</i> iJR904 downloaded from the <a href="http://bigg.ucsd.edu/bigg/home.pl">BiGG model database</a><a href="#ref1">[1]</a>. We chose this model because it contains all the metabolites involved in the enterobactin production pathway.
</p>
</p>
<p>
<p>
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This model contains 4037 reactions and 625 metabolites but lacks the <a href="http://www.genome.jp/dbget-bin/www_bget?rn:R07644">enterobactin syntase</a> (the last reaction of the enterobactin production pathway). Thus we extended it, adding this reactions and an enterobactin output reaction to be able to consider enterobactin as a component of the model. The inserted reactions are the following:
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This model contains 4037 reactions and 625 metabolites but lacks the <a href="http://www.genome.jp/dbget-bin/www_bget?ec:6.3.2.14">enterobactin synthase</a> (the last reaction of the enterobactin production pathway). We thus extended the model by adding three new reactions :
</p>
</p>
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<tr>
<tr>
<td>ENTSYNTH</td>
<td>ENTSYNTH</td>
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<td>6 ATP + 3 2,3-Dihydroxybenzoate + 3 L-Serine <=> Enterobactin + 6 AMP + 6 Diphosphate</td>
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<td>6 ATP + 3 2,3-DIHYDROXYBENZOATE + 3 L-SERINE => ENTEROBACTIN + 6 AMP + 6 DIPHOSPHATE</td>
</tr>
</tr>
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<tr>
<tr>
<td>ENTOUT</td>
<td>ENTOUT</td>
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<td>  => -1 Enterobactin</it>
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<td>  => -1 ENTEROBACTIN</it>
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</td>
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</tr>
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<tr>
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<td>CHORIN</td>
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<td>  => +1 CHORISMATE</it>
</td>
</td>
</tr>
</tr>
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<p>
<p>
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The modified <i>E. Coli</i> iJR904 model containing these two new reactions can be found in the download section at the bottom of the page.
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The last two reactions are respectively output flux of ENTOUT and intput flux of CHORIN that are artificial reactions used to measure and interact with our system.
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The modified <i>E. coli</i> iJR904 model containing these two new reactions can be found in the download section at the bottom of the page.
</p>
</p>
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<p>
<p>
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As an analysis of the whole network is too complex, we focused our analysis on the subnetwork presented in <a href="#Fig2">Figure 2</a>. We considered the CHORISMIC ACID (hereafter called CHORISMATE) as the first step of the anterobactin production pathway. This is justified by the fact that chorismate can be bought from any compound supplier (for example <a href="http://www.sigmaaldrich.com/catalog/product/aldrich/c1761?lang=fr&region=FR">sigma</a>).
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The ENTEROBACTIN production pathway in kegg starts with the CHORISMATE (or CHORISMIC ACID) compound. Hence, we restricted our study to this specific sub-network, presented in <a href="#Fig2">Figure 2</a> of the whole metabolic network. We did not directly investigated the previous reactions leading to CHORISMATE production. They are taken into accound in the whole metabolic network model but we did not try to act on these. It is also worth noting that the CHORISMATE compound can be bought from any chemical compound supplier (for example <a href="http://www.sigmaaldrich.com/catalog/product/aldrich/c1761?lang=fr&region=FR">sigma</a>).
</p>
</p>
<p>
<p>
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We also found that  it would also be possible to by the 2-3-DIHYDROXYBENZOATE compound from the <a href="http://www.sigmaaldrich.com/catalog/product/aldrich/126209?lang=fr&region=FR">same provider</a>. This compound may be very interesting to test our constructions later as the last precursor of the pathway. For the same reason, and because it does participate in any other reaction in the used model, this compound has no interest in our metabolic modeling.
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We also found that  it would also be possible to by the 2-3-DIHYDROXYBENZOATE compound from the <a href="http://www.sigmaaldrich.com/catalog/product/aldrich/126209?lang=fr&region=FR">same provider</a>. This compound may be very interesting to test our constructions later as the last precursor of the pathway. For the same reason, and because it is not consumed by any other metabolic reaction present in the current model.
</p>
</p>
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<div class="captionedPicture" style="float:left;">
<div class="captionedPicture" style="float:left;">
<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/7/76/Metabolic_network_entero_full.png">
<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/7/76/Metabolic_network_entero_full.png">
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/7/76/Metabolic_network_entero_full.png" alt="Considered E.Coli Metabolic Reaction Network." class="Picture"/>
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/7/76/Metabolic_network_entero_full.png" width="800px" height="400px" alt="Considered <i>E. coli</i> Metabolic Reaction Network." class="Picture"/>
</a>
</a>
<div class="caption">
<div class="caption">
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<b>Figure 2:</b> <p>The considered subnetwork of metabolic reactions of E.coli. Red arrows are reaction consuming compounds used in the ENTEROBACTIN production pathway. Arrows : reactions, circles : enzymes.</p>
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<b>Figure 2:</b> <p>The considered subnetwork of metabolic reactions of <i>E. coli</i>. Red arrows are reaction consuming compounds used in the ENTEROBACTIN production pathway. Arrows : reactions, circles : enzymes.</p>
</div>
</div>
</div>
</div>
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<p>
<p>
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The metabolic model of <i>E. Coli</i> is represented as a stoichiometry matrix S representing the metabolic network. Our modified version has the size 4039 (reactions) * 625 (compounds). The unknown is the flux distribution  vector v, a 4039-vector representing the flow of matter (mmol/gDW/h) going through each reactions.
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The metabolic model of <i>E. Coli</i> is based on a stoichiometry matrix S representing the metabolic network. Our modified version has the size 4039 (reactions) * 625 (compounds). The unknown is the flux distribution  vector v, a column-vector of size 4039 representing the flow of matter (mmol/gDW/h) going through each reaction.
</p>
</p>
<p>
<p>
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The Flux Balance Analysis method is about finding this flux repartition vector v given an objective function to optimize (usually the growth rate) and a set of constraints of fluxes.
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The Flux Balance Analysis method<a href="#ref2">[2]</a> is about finding this flux repartition vector v given an objective function to optimize (usually the growth rate) and a set of constraints on the fluxes values.
</p>
</p>
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<p>
<p>
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The Two assumptions at the heart of the method are the following :
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The two assumptions at the heart of the method are the following :
<ol>
<ol>
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<p>
<p>
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The FBA method use a representation of the metabolic reaction network in the form of a stoichiometry matrix S where :
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The FBA method uses a representation of the metabolic reaction network in the form of a stoichiometry matrix S where :
<ul>
<ul>
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<p>
<p>
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The FBA problem is then formulated as a maximisation problem under some constraints :
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The FBA problem is then formulated as a maximisation problem under some constraints:
</p>
</p>
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</ul>
</ul>
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The values for the lower and upper bounds on the flux of each reactions are either deduced from experiments or put to a very high value when unknown (most of the time) :
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The values for the lower and upper bounds on the flux of each reactions are either deduced from experiments or put to a very high value when unknown (most of the time):
</p>
</p>
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<p>
<p>
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To perform the simulations we decided to use the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">cobrapy software</a><a href="#ref2">[2]</a> in conjunction with the <a href="http://www.gnu.org/software/glpk/">GLPK</a> (GNU Linear Progamming Kit) linear programming solver.
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To perform the simulations we decided to use the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">cobrapy software</a><a href="#ref3">[3]</a> in conjunction with the <a href="http://www.gnu.org/software/glpk/">GLPK</a> (GNU Linear Progamming Kit) linear programming solver.
</p>
</p>
<p>
<p>
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All the simulations of model modifications were made through python scripts, available at the end of the document.
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All the simulations and model modifications were made through python scripts, available at the end of the document.
</p>
</p>
<h2>Results</h2>
<h2>Results</h2>
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<h3>Question 1: Interractions between the artifitial system and the metabolism of E.coli</h3>
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With this formalism we were able to answer to the questions presented at the begining of the page. The results are presented on the two following pages:
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<p>
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<em>Model</em> : <i>E. Coli</i> iJR904 with addition of ENTSYNTH and ENTOUT reactions.
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</p>
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<p>
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In order to test whether the system interacts with the metabolism of the bacteria we applied the FBA with a modification to the objective function:
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</p>
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<div align="center" style="margin:1%;">
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<img src="https://static.igem.org/mediawiki/2013/2/28/Optim_q1.png" alt="fba optimization function for testing q1" />
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</div>
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<p>
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By varying i between 0 and 1 we can vary the contributions of enterobacting production and biomass growth.
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</p>
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<h3>Simulation</h3>
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<p>
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The <a href="#Fig3">Figure 3</a> presents the results of the simulation following the previous settings.
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</p>
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<center>
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<div class="captionedPicture" style="float:left;">
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<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/8/80/Q1_curve.png">
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/8/80/Q1_curve.png" alt="Considered E.Coli Metabolic Reaction Network." class="Picture"/>
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</a>
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<div class="caption">
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<b>Figure 3:</b> <p>Fluxes as function of the i coefficient of the modified objective function. Dotted lines : Important fluxes; Solid lines : Reactions belonging to the ENTEROBACTIN biosynthesis pathway; Dashed lines : Other reactions consuming important metabolites.</p>
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</div>
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</div>
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<a id="Fig3"></a>
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<div style="clear: both;"></div>
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</center>
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<p>
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<ul>
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<li>Setup : i varied from 0 to 1 with a step of 0.01.</li>
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<li>The code for this model can be found at the bottom of the page</li>
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</ul>
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</p>
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<h3>Interpretation</h3>
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<p>
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The graph is divided in two parts :
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</p>
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<h4>i < 0.303</h4>
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<p>
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In this part, no ENTOUT flux is present, hence there is no production of enterobactin. In the other hand there are two non-zero constant fluxes, the CHORISMATE MUTASE (at 0.28 mmol/gDW/h) and DEOXYCHORISMATE SYNTHASE (at 0.04 mmol/gDW/h). The objective function is linearly decreasing with i meaning that the system does not change in this part.
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</p>
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<h4>i = 0.303</h4>
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<p>
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For this value of i there is a brutal change in the different fluxes :
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<ul>
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<li>CHORMISMATE MUTASE flux goes to 0 mmol/gDW/h</li>
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<li>DEOXYCHORISMATE SYNTHASE flux goes to 0 mmol/gDW/h</li>
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<li>ISOCHORISMATE SYNTHASE, ISOCHORISMATASE, ENTOUT fluxes goes to their maximal values 2.1 mmol/gDW/h</li>
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</ul>
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The objective function f stops decreasing.
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</p>
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<h4>i > 0.303</h4>
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<p>
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After this value of i the system stays the same as proved by the linear increase of the objective function with i.
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</p>
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<h3>Conclusion</h3>
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<p>
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The production of enterobactin modifies the flux distribution for only two reactions out of 5 (the others have a nul flux) : CHORMISMATE MUTASE and DEOXYCHORISMATE SYNTHASE.<br/>
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Thus <em>there is an interaction between the enterobactin production system and the <i>E. Coli</i> metabolism</em> that happen at the level of these reactions.
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</p>
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<h2>Studying the perturbation</h2>
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<p>
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<em> model:</em> <i>E. Coli</i> iJR904 wild type<br/>
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</p>
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<p>
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In order to understand the type of perturbation happening two new simulations were created in which the upper bound of the fluxes going through those reactions are progressively reduced until attaining 0. The response of the system (the biomass function) is plotted against these changes in upper bound flux value.
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</p>
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<h3>Upper bound modification for the CHORMISMATE MUTASE reaction</h3>
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<h4>Simulation</h4>
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<p>
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The results of the simulation are presented in <a href="#Fig4>Figure 4</a>.
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</p>
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<center>
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<div class="captionedPicture" style="float:left;">
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<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/f/f9/Q1_chorismate_mutase.png">
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/f/f9/Q1_chorismate_mutase.png" alt="results for chorismate mutase" class="Picture"/>
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</a>
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<div class="caption">
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<b>Figure 4:</b> <p>CHORISTMATE MUTASE reaction flux and growth rate as a function of the upper bound of the CHORISMATE MUTASE flux.</p>
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</div>
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</div>
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<a id="Fig4"></a>
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<div style="clear: both;"></div>
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</center>
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<p>
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<ul>
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<li>Setup: upperbound from 1 mmol/gDW/h to 0 with a step of 0.01</li>
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<li>The code for this model can be found at the bottom of the page</li>
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</ul>
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</p>
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<h4>Interpretation</h4>
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<p>
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As can be seen on <a href="#Fig4>Figure 4</a>, the relationship between the CHORISMATE MUTASE flux and the growth rate is linear, hence this reaction is central to the metabolism of E.coli.
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</p>
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<p>
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It appears that the CHORISMATE MUTASE reaction takes CHORISMATE and produces PREPHENATE, a compound participating the production of tryphosphate, an amino acid. Hence one must be very careful no to perturb this reaction, note that when the flux is not perturbated, the growth rate value is the maximal predicted one.
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</p>
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<h3>Upper bound modification for the DEOXYCHORISMATE SYNTHASE</h3>
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<h4>Simulation</h4>
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<p>
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The results of the simulation are presented in <a href="#Fig5>figure 5</a>.
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</p>
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<center>
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<div class="captionedPicture" style="float:left;">
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<a title="Nom Lien" https://static.igem.org/mediawiki/2013/b/b3/Q1_deoxychorismate_synthase.png">
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/b/b3/Q1_deoxychorismate_synthase.png" alt="deoxychorismate synthase dependency" class="Picture"/>
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</a>
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<div class="caption">
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<b>Figure 5:</b> <p>DEOXYCHORISTMATE SYNTHASE reaction flux and growth rate as a function of the upper bound of the CHORISMATE MUTASE flux.</p>
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</div>
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</div>
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<a id="Fig5"></a>
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<div style="clear: both;"></div>
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</center>
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<p>
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<ul>
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<li>Setup: upperbound from 1 mmol/gDW/h to 0 with a step of 0.01</li>
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<li>The code for this model can be found at the bottom of the page</li>
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</ul>
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</p>
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<h4>Interpretation</h4>
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<p>
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The dynamic of the curve in <a href="#Fig5">Figure 5</a> is the same as the one in <a href="#Fig4">Figure 4</a>, except that the dependency relation between the growth rate and the DEOXYCHORISMATE SYNTHASE is higher (as seen by the slope of the growth rate curve in its ascending part).
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</p>
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<p>
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Again, this may be explained by the fact that the DEOXYCHORISMATE SYNTHASE reaction produces L-GLUTAMATE whichi is a central metabolite of the bacteria.
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</p>
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<h3>Conclusion on the perturbations</h3>
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<p>
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We observed linear dependencies of the growth rate for the two perturbated reactions DEOXYCHORISMATE SYNTHASE and CHORISMATE MUTASE which means that reducing the flux of any of these reactions may have very important consequences on the growth rates of the bacterias.
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</p>
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<p>
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Hence, <em>the CHORISMATE metabolite can be considered as a limiting factor of the enterobacting bionsythesis pathway</em>.
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</p>
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<h2>Questions 2 and 3 : How many chorismate to add ?</h2>
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<p>
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<em>model</em>:  E.Coli iJR904 with addition of ENTSYNTH, ENTOUT and CHORIN (intake of CHORISMATE) reactions.
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</p>
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<p>
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This section is about finding the good quantity of CHORISMATE to give to the bacteria for having a maximal production of enterobactin.
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</p>
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<p>
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To answer this question, we set up a simulation using the same objective function as the first simulation :
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</p>
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<div align="center" style="margin:1%;">
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<img src="https://static.igem.org/mediawiki/2013/2/28/Optim_q1.png" alt="fba optimization function for testing q1" />
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</div>
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<p>
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At the same time we simulate an addition of CHORISMATE into the medium by adding an input flux of CHORISMATE named CHORIN. The ENTOUT flux (production of ENTEROBACTIN) is then monitored as a function of both parameters in order to get the best quantity of CHORISMATE to add in all conditions.
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</p>
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<h4>Simulation</h4>
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<p>
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The results of the simulation are presented in <a href="#Fig6>figure 6</a>.
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</p>
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<center>
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<div class="captionedPicture" style="float:left;">
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<a title="Nom Lien" href="https://static.igem.org/mediawiki/2013/7/7b/Q2_q3_chorin_i_entout.png">
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<img alt="Nom Lien" src="https://static.igem.org/mediawiki/2013/7/7b/Q2_q3_chorin_i_entout.png" alt="CHORIN vs i VS ENTOUT" class="Picture"/>
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</a>
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<div class="caption">
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<b>Figure 6:</b> <p>ENTOUT flux (ENTEROBACTIN output flux) as a function of CHORIN (CHORISMATE input flux) and i coefficient of the modified objective function.</p>
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</div>
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</div>
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<a id="Fig6"></a>
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<div style="clear: both;"></div>
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</center>
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<p>
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<ul>
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<li>Setup: upperbound from 1 mmol/gDW/h to 0 with a step of 0.01</li>
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<li>The code for this model can be found at the bottom of the page</li>
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</ul>
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</p>
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<h4>Interpretation</h4>
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<p>
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As we can see on <a href="#Fig6">Figure 6</a>, the relationship between the ENTOUT flux and the CHORIN flux is linear whatever the i choosen. A stable region is reached for the ENTOUT flux at a flux for CHORIN of about 17 mmol/gDW/h for all the i. Hence this result gives an idea of the quantity of CHORISMATE to add to the medium in order to maximize the production of ENTEROBACTIN.
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</p>
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<p>
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Plus, the dynamic of the graph becomes more regular as more CHORISMATE is added, this means that adding a CHORISMATE input flux successfuly counter the effect of the consumption of CHORISMATE by the enterobacting production pathway.
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</p>
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<h3>Conclusion</h3>
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This model gives two interesting results :
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<ol>
<ol>
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<li>The dynamic of the ENTOUT flux as a function of CHORIN and i is very regular;</li>
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<li>Metabolic Interactions on <a href="https://2013.igem.org/Team:Evry/interactions">this</a> page.</li>
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<li>A flat region for the ENTOUT flux is reached for all values of i for about 17 mmol/gDW/h of CHORISMATE.</li>
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<li>The effects of supplying chorismate on <a href="https://2013.igem.org/Team:Evry/supply">this</a> page.</li>  
</ol>
</ol>
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<p>
<p>
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First, we wanted to know if adding new synthetic constructs would perturb the metabolism of E.coli. This was answered by a first model plotting a modified objective function optimizing both ENTEROBACTIN production and growth rate. The model showed that the enterobactin pathway would perturb the CHORISMATE MUTASE and DEOXYCHORISMATE SYNTHASE reactions.
+
First, we wanted to know if adding new synthetic constructs would perturb the metabolism of <i>E. coli</i>. This was answered by a first model plotting a modified objective function optimizing both ENTEROBACTIN production and growth rate. The model showed that the <em>enterobactin pathway perturbs the CHORISMATE MUTASE and DEOXYCHORISMATE SYNTHASE reactions</em> that belong to <i>E. coli</i> central metabolism.
</p>
</p>
<p>
<p>
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In two other models, we showed that these two reactions were very crucial for the bacteria, as a reduction of their maximal upper flux lead to a direct (linear) decrease of the growth rate function. Resulting in the extreme case with a nul growth rate for a nul flux.
+
In two other models, we showed that the <em>CHORISMATE MUTASE AND DEOXYCHORISMATE SYNTHASE were very crucial for the bacteria</em>, as a reduction of their maximal upper flux lead to a direct (linear) decrease of the growth rate function. No flux going through any of these reactions result in no predicted growth of the bacteria.
</p>
</p>
<p>
<p>
-
Finally, we analysed how we could find a workaround to this problem by adding some CHORISMATE to the medium. This situation was represented in a third model with a CHORISMATE input flux that we plotted against the modified objective function. This model clearly showed that the ENTEROBACTIN flux attained a maximal stable region for any value of the objective function starting from 17 mmol/gDW/h of CHORISMATE. Finally, the dynamic of the model is very smoother as more CHORISMATE is added, showing that this addition could paliate the effects of the addition of the ENTEROBACTIN pathway.
+
Finally, we analysed how we could find a workaround to this problem by <em>adding some CHORISMATE to the medium</em>. This situation was presented in a third model with a CHORISMATE input flux that we plotted against the modified objective function. This model clearly showed that the ENTEROBACTIN flux attained a maximal stable region for any value of the objective function starting from <em>17 mmol/gDW/h of CHORISMATE</em>. Finally, the dynamic of the ENTOUT flux is being smoother as more CHORISMATE is added.
</p>
</p>
<p>
<p>
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In the end, this FBA model shows that it is possible to paliate the effect of our construction on the metabolism of E.Coli by adding some CHORISMATE to the medium. <em>This justifies the other models developed that did the assumption that no metabolite was limiting.</em>
+
In the end, this FBA model shows that it is possible to counter-balance the effects of our synthetic construction on the metabolism of <i>E. coli</i> by adding some CHORISMATE to the medium.
 +
 
 +
On the modeling point of view, these results are in agreement with one of the central assumption made in the other model: no metabolites of the pathway is limiting.
</p>
</p>
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<ol>
<ol>
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<li>Wild-Type E.Coli iJR904</li>
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<li>Wild-Type <i>E. coli</i> iJR904</li>
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<li>ENTOUT(enterobactin outflux) + ENTSYNTH(enterobactin synthase) E.Coli iJR904</li>
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<li>ENTOUT(enterobactin outflux) + ENTSYNTH(enterobactin synthase) <i>E. coli</i> iJR904</li>
</ol>
</ol>
These models can be found in <a href="https://static.igem.org/mediawiki/2013/b/bc/Evry_ecoli_iJR904_models.zip">this tarball</a>.
These models can be found in <a href="https://static.igem.org/mediawiki/2013/b/bc/Evry_ecoli_iJR904_models.zip">this tarball</a>.
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<div id="citation_box">
<div id="citation_box">
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  <p id="references">References:</p>
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  <p id="references"><b>References:</b></p>
  <ol>
  <ol>
<li><a id="ref1"></a>Schellenberger, J., Park, J. O., Conrad, T. C., and Palsson, B. Ø., "BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions", BMC Bioinformatics, 11:213, (2010).</li>
<li><a id="ref1"></a>Schellenberger, J., Park, J. O., Conrad, T. C., and Palsson, B. Ø., "BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions", BMC Bioinformatics, 11:213, (2010).</li>
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<li><a id="ref2></la>Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. 2013  COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Bio 7:74.</li>
+
<li><a id="ref2"></a>Orth, Jeffrey D., Ines Thiele, and Bernhard Ø. Palsson. "What is flux balance analysis?." Nature biotechnology 28.3 (2010): 245-248.</li>
 +
<li><a id="ref3"></a>Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. 2013  COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Bio 7:74.</li>
  </ol>
  </ol>
</div>
</div>

Latest revision as of 19:56, 28 October 2013

Iron coli project

Flux model

Introduction

In this part of our modeling work we focus on genome scale analysis of the enterobactin production pathway. A major concern about our system is its non-orthogonality with the natural metabolic network of the cells : E. coli already possesses the genes for producing enterobactins. Therefore we wanted to assess the possible interactions between our system and the bacterial metabolism.

Observations

This model stems from the following observations :

  • Enterobactin production pathway is a metabolic process;
  • Any of the involved metabolites may limit the rate of the reactions.
The Figure 1 presents the biochemical pathway for producing the enterobactin compound in E. coli.

Nom Lien
Figure 1:

The ENTEROBACTIN biosynthesis pathway. Arrows : reactions, circles : enzymes.

As can be seen in the Figure 1 the pathway is 4 steps long and composed of six different enzymes. Hence there exists 4 possible metabolites which concentration may be limiting :

  • Chorismate
  • Isochorismate
  • 2,3-dihydroxy-2,3-dihydrobenzoate
  • 2,3-dihydroxybenzoat

Goals

We highlighted two main kind of interactions between the bacteria and our system :

  1. The synthetic system interacts with the bacterial metabolism. Leading to a depletion of the metabolites involved in the pathway for the other (possibly essential) metabolic reactions of the cell.
  2. The other way round, the metabolic reactions could prevent our synthetic system to work as expected by limiting the quantity of the involved metabolites available.
From these assumptions we formulated the following questions :
  • Is the metabolic model of E. coli able to provide any information about the possible interactions between our system and the metabolism?
  • Is the concentration of any metabolite limiting ?
  • In the latter case, what is the quantity of precursor that should be added in the medium?

Materials and Methods

Model

We used the metabolic model E. coli iJR904 downloaded from the BiGG model database[1]. We chose this model because it contains all the metabolites involved in the enterobactin production pathway.

This model contains 4037 reactions and 625 metabolites but lacks the enterobactin synthase (the last reaction of the enterobactin production pathway). We thus extended the model by adding three new reactions :

Name Formula
ENTSYNTH 6 ATP + 3 2,3-DIHYDROXYBENZOATE + 3 L-SERINE => ENTEROBACTIN + 6 AMP + 6 DIPHOSPHATE
ENTOUT => -1 ENTEROBACTIN
CHORIN => +1 CHORISMATE

The last two reactions are respectively output flux of ENTOUT and intput flux of CHORIN that are artificial reactions used to measure and interact with our system. The modified E. coli iJR904 model containing these two new reactions can be found in the download section at the bottom of the page.

Network Reduction

The ENTEROBACTIN production pathway in kegg starts with the CHORISMATE (or CHORISMIC ACID) compound. Hence, we restricted our study to this specific sub-network, presented in Figure 2 of the whole metabolic network. We did not directly investigated the previous reactions leading to CHORISMATE production. They are taken into accound in the whole metabolic network model but we did not try to act on these. It is also worth noting that the CHORISMATE compound can be bought from any chemical compound supplier (for example sigma).

We also found that it would also be possible to by the 2-3-DIHYDROXYBENZOATE compound from the same provider. This compound may be very interesting to test our constructions later as the last precursor of the pathway. For the same reason, and because it is not consumed by any other metabolic reaction present in the current model.

Nom Lien
Figure 2:

The considered subnetwork of metabolic reactions of E. coli. Red arrows are reaction consuming compounds used in the ENTEROBACTIN production pathway. Arrows : reactions, circles : enzymes.

Flux Balance Analysis

The metabolic model of E. Coli is based on a stoichiometry matrix S representing the metabolic network. Our modified version has the size 4039 (reactions) * 625 (compounds). The unknown is the flux distribution vector v, a column-vector of size 4039 representing the flow of matter (mmol/gDW/h) going through each reaction.

The Flux Balance Analysis method[2] is about finding this flux repartition vector v given an objective function to optimize (usually the growth rate) and a set of constraints on the fluxes values.

Assumptions of the model

The two assumptions at the heart of the method are the following :

  1. steady state: The fluxes are considered to have attained a static equilibrium value and do not change through time.
  2. No enzyme saturation: The enzymes are supposed to be not saturated, the number of enzymes is always greater than the number of the corresponding reactions happening.

Formalism

The FBA method uses a representation of the metabolic reaction network in the form of a stoichiometry matrix S where :

  • Each row corresponds to a reaction R_i
  • Each column corresponds to a metabolite C_j
The definition of S is :

stoichiometry matrix construction

The FBA problem is then formulated as a maximisation problem under some constraints:

FBA formulation

where :

  • v is the vector of unknown reaction fluxes
  • c is a vector of constants defining the objective function
  • S is the stoichiometry matrix
  • lowerbound and upperbound are vector of constraints (minimal and maximal flux values for each reactions)
The values for the lower and upper bounds on the flux of each reactions are either deduced from experiments or put to a very high value when unknown (most of the time):

FBA boundaries

Tunning these boundaries allows to represent different experimental conditions, for example reducing an upper bound to a low value may represent a loss of reaction flux due to the scarcity of a certain compound.

Such optimization is then realized through a linear programming algorithm finding an approximated solution of the real optimal flux distribution (the distribution maximizing the objective function).

Software used

To perform the simulations we decided to use the cobrapy software[3] in conjunction with the GLPK (GNU Linear Progamming Kit) linear programming solver.

All the simulations and model modifications were made through python scripts, available at the end of the document.

Results

With this formalism we were able to answer to the questions presented at the begining of the page. The results are presented on the two following pages:
  1. Metabolic Interactions on this page.
  2. The effects of supplying chorismate on this page.

Conclusion

The metabolic models presented in this sections have been used to answer the general questions we had about our system.

First, we wanted to know if adding new synthetic constructs would perturb the metabolism of E. coli. This was answered by a first model plotting a modified objective function optimizing both ENTEROBACTIN production and growth rate. The model showed that the enterobactin pathway perturbs the CHORISMATE MUTASE and DEOXYCHORISMATE SYNTHASE reactions that belong to E. coli central metabolism.

In two other models, we showed that the CHORISMATE MUTASE AND DEOXYCHORISMATE SYNTHASE were very crucial for the bacteria, as a reduction of their maximal upper flux lead to a direct (linear) decrease of the growth rate function. No flux going through any of these reactions result in no predicted growth of the bacteria.

Finally, we analysed how we could find a workaround to this problem by adding some CHORISMATE to the medium. This situation was presented in a third model with a CHORISMATE input flux that we plotted against the modified objective function. This model clearly showed that the ENTEROBACTIN flux attained a maximal stable region for any value of the objective function starting from 17 mmol/gDW/h of CHORISMATE. Finally, the dynamic of the ENTOUT flux is being smoother as more CHORISMATE is added.

In the end, this FBA model shows that it is possible to counter-balance the effects of our synthetic construction on the metabolism of E. coli by adding some CHORISMATE to the medium. On the modeling point of view, these results are in agreement with one of the central assumption made in the other model: no metabolites of the pathway is limiting.

Models and Scripts

Metabolic Models

The two metabolic models used are :

  1. Wild-Type E. coli iJR904
  2. ENTOUT(enterobactin outflux) + ENTSYNTH(enterobactin synthase) E. coli iJR904
These models can be found in this tarball.

Scripts

The scripts used to generates the curves on this page can be found on this archive. They are Python scripts with distinct names corresponding to the simulation they represent.

References:

  1. Schellenberger, J., Park, J. O., Conrad, T. C., and Palsson, B. Ø., "BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions", BMC Bioinformatics, 11:213, (2010).
  2. Orth, Jeffrey D., Ines Thiele, and Bernhard Ø. Palsson. "What is flux balance analysis?." Nature biotechnology 28.3 (2010): 245-248.
  3. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. 2013 COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Bio 7:74.