Revision as of 18:07, 26 July 2017

Modeling

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.

Metabolic Modeling

Recent advances in computational biology include whole-genome metabolic networks. They describe the flow of metabolites within a particular organism. Using so called constraint-based method allows to make quantitative predictions about the phenotype while eliminating many of the complex parameters. Of particular interest to our project is flux balance analysis (FBA), which allows to predict the optimal steady-state biomass flux, which is directly correlated with cell growth rate and is the most likely observed phenotype. One major advantage of FBA is that it does not require knowledge of enzymatic parameters. The mathematics of FBA is elucidated in this figure.

One of the primary questions for the project is the following: will the synthetic P. denitrificans strain grow better than the unmodified one in presence of ammonia? To answer this question, we performed comparative analysis of the two Paracoccus strains using FBA on the whole-genome metabolic models. The analysis pipeline involved a slew of open-source computational tools, which we will describe below.

First, we reconstructed a metabolic model of Paracoccus denitrificans strain DSM 413 on a complete medium using ModelSEED. A complete medium is such that any nutrient, including ammonia, is available for uptake. Thus, the set of reactions included in the model is the biggest of all possible sets. Although the largest, this set is incomplete. In the next step, the model was gapfilled with all the reactions necessary for measurable cell growth.

The nature of our project dictates that we must be able to manually include several reactions, metabolites and genes (e.g. oxygenation of ammonia by the AMO enzyme complex) into the model. Such functionality is not available in ModelSEED. To implement this, we turned to COBRApy: Constraint-Based Reconstruction and Analysis package written in Python. COBRApy does not natively work with ModelSEED models. To overcome this, we used Mackinac package to convert the ModelSEED model into COBRApy. Using COBRApy, we can then add the new ingredients into the model and perform FBA to compare the biomass fluxes, and hence the growth rates, of the two Paracoccus strains. The script is available here.

First, the FBA was run on the gapfilled unmodified model, which initially contained 1550 reactions and 1556 metabolites. The optimal biomass flux was found to be \( 224.3248 \frac{\text{mmol metabolite}}{\text{g dry weight}\cdot\text{h}} \). Next, we add the reactions. Below is the list of all reactions added to the model. \(\ce{Q}\) and \(\ce{QH_2}\) represent ubiquinone and ubiquinol, respectively. \( \text{UqO} \) is the ubiquinone oxidoreductase enzyme which catalyzes the last reaction. \[ \ce{NH_3 + QH_2 + O_2 ->[\text{AMO}] H_2O + Q + NH_2OH} \] \[ \ce{NH_3 + NAD + H_2O ->[\text{AMO}] 2H^+ + NADH + NH_2OH} \] \[ \ce{NH_2OH + O_2 ->[\text{HAO?}] NO_2^- + H^+ + H_2O} \] \[ \ce{NH_2OH + 2Q + H_2O ->[\text{HAO}] NO_2^- + 2QH_2} \] \[ \ce{NO_2^- + NAD + H_2O ->[\text{nir}] H^+ + NO_3^- + NADH} \] \[ \ce{QH_2 ->[\text{UqO}] 2H^+ + Q} \] The optimal biomass flux of the modified model is \( 228.6980 \frac{\text{mmol metabolite}}{\text{g dry weight}\cdot\text{h}}\), with

ModelSEED

1

Gold Medal Criterion #3

To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your judging form. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.

Please see the 2017 Medals Page for more information.

Best Model Special Prize

To compete for the Best Model prize, please describe your work on this page and also fill out the description on the judging form. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.

You must also delete the message box on the top of this page to be eligible for the Best Model Prize.

Inspiration

Here are a few examples from previous teams:

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 <p0> Recent advances in computational biology include whole-genome metabolic networks. They describe the flow of metabolites within a particular organism. Using so called <emph>constraint-based method</emph> allows to make quantitative predictions about the phenotype while eliminating many of the complex parameters. Of particular interest to our project is <b>flux balance analysis (FBA)</b>, which allows to predict the optimal steady-state biomass flux, which is directly correlated with cell growth rate and is the most likely observed phenotype. One major advantage of FBA is that it does not require knowledge of enzymatic parameters. The mathematics of FBA is elucidated in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911401/figure/F2/">this figure.</a></p0>
 <br></br>
-<p0> One of the primary questions for the project is this: <b>will the synthetic <i>P. denitrificans</i> strain grow better than the unmodified one in presence of ammonia?</b> To answer this question, we performed comparative analysis of the two strains using FBA on the whole-genome metabolic models. The analysis pipeline involved a slew of open-source computational tools, which we will describe below.</p0>
+<p0> One of the primary questions for the project is the following: <b>will the synthetic <i>P. denitrificans</i> strain grow better than the unmodified one in presence of ammonia?</b> To answer this question, we performed comparative analysis of the two <i>Paracoccus</i> strains using FBA on the whole-genome metabolic models. The analysis pipeline involved a slew of open-source computational tools, which we will describe below.</p0>
 <center><img src="https://static.igem.org/mediawiki/2017/7/78/Virginia--mod_pipeline.png" alt="" usemap="#map1500485992320" style="width:812px;height:271px"></center>
 <map id="map1500485992320" name="map1500485992320"><area shape="rect" coords="50,50,232,207" title="" alt="" href="http://modelseed.org/" target="_parent"><area shape="rect" coords="317,49,498,206" title="" alt="" href="https://github.com/mmundy42/mackinac" target="_parent"><area shape="rect" coords="585,49,769,207" title="" alt="" href="https://github.com/opencobra/cobrapy" target="_parent"><area shape="rect" coords="810,269,811,270" alt="Image HTML map generator" title="HTML Map creator" href="http://www.html-map.com/" target="_self"></map>
-<p0> First, we reconstructed a metabolic model of <i>Paracoccus denitrificans strain DSM 413</i> on complete media using ModelSEED. A complete medium is such that any nutrient, including ammonia, is available for uptake. Thus, the set of reactions included in the model is the biggest of all possible sets. Although the largest, this set is incomplete. In the next step, the model was gapfilled with all the reactions necessary for measurable cell growth. </p0>
+<p0> First, we reconstructed a metabolic model of <i>Paracoccus denitrificans strain DSM 413</i> on a complete medium using ModelSEED. A complete medium is such that any nutrient, including ammonia, is available for uptake. Thus, the set of reactions included in the model is the biggest of all possible sets. Although the largest, this set is incomplete. In the next step, the model was gapfilled with all the reactions necessary for measurable cell growth. </p0>
 <br></br>
-<p0> The nature of our project dictates that we must be able to manually include several reactions, metabolites and genes (e.g. oxygenation of ammonia by the AMO enzyme complex) into the model. Such functionality is not available in ModelSEED. To implement this, we turned to COBRApy: Constraint-Based Reconstruction and Analysis package written in Python. COBRApy does not natively work with ModelSEED models. To overcome this, we used Mackinac package to import the ModelSEED model into COBRApy. Using COBRApy, we can add the new ingredients into the model and then run FBA to compare the biomass fluxes, and hence the growth rates, of the two <i>Paracoccus</i> strains. The script is available <a href="https://2017.igem.org/Team:Virginia/Model/Files/CobraPD-LB.py">here</a>.</p0>
+<p0> The nature of our project dictates that we must be able to manually include several reactions, metabolites and genes (e.g. oxygenation of ammonia by the AMO enzyme complex) into the model. Such functionality is not available in ModelSEED. To implement this, we turned to COBRApy: Constraint-Based Reconstruction and Analysis package written in Python. COBRApy does not natively work with ModelSEED models. To overcome this, we used Mackinac package to convert the ModelSEED model into COBRApy. Using COBRApy, we can then add the new ingredients into the model and perform FBA to compare the biomass fluxes, and hence the growth rates, of the two <i>Paracoccus</i> strains. The script is available <a href="https://2017.igem.org/Team:Virginia/Model/Files/CobraPD-LB.py">here</a>.</p0>
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