Difference between revisions of "Team:Virginia/Model"

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<h2> Metabolic Modeling</h2>
 
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<p0> Recent advances in computational biology led to emergence of databases with whole-genome metabolic networks, which for simplicity we will call models. They describe the flow of metabolites within a particular organism. Applying <em>constraint-based methods</em> to the model the allows to make quantitative predictions about the phenotype while eliminating many of the complex parameters. Of particular interest to our project is <em>flux balance analysis (FBA)</em>, which allows to predict the optimal steady-state biomass flux, which is often correlated with cell growth rate<sup>[1]</sup> 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<sup>[2]</sup></a></p0>
 
<p0> Recent advances in computational biology led to emergence of databases with whole-genome metabolic networks, which for simplicity we will call models. They describe the flow of metabolites within a particular organism. Applying <em>constraint-based methods</em> to the model the allows to make quantitative predictions about the phenotype while eliminating many of the complex parameters. Of particular interest to our project is <em>flux balance analysis (FBA)</em>, which allows to predict the optimal steady-state biomass flux, which is often correlated with cell growth rate<sup>[1]</sup> 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<sup>[2]</sup></a></p0>
 
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Revision as of 15:25, 15 August 2017


Metabolic Modeling

Recent advances in computational biology led to emergence of databases with whole-genome metabolic networks, which for simplicity we will call models. They describe the flow of metabolites within a particular organism. Applying constraint-based methods to the model the 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 often correlated with cell growth rate[1] 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[2]

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 a whole-genome metabolic model. The analysis pipeline involved a slew of open-source computational tools, which we will describe below.
Image HTML map generator First, we reconstructed a metabolic model of Paracoccus denitrificans strain DSM 413 on a complete medium using ModelSEED[3]. 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[4] package written in Python. COBRApy does not natively work with ModelSEED models. To overcome this, we used Mackinac package[5] 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 \( 224.3248 (\text{g dry weight}\cdot\text{h})^{-1} \). Next, we added 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. \[ \ce{NH_3 + QH_2 + O_2 ->[\text{AMO}] H_2O + Q + NH_2OH} \] \[ \ce{NH_3 + NAD + H_2O ->[\text{AMO}] 2H^+ + NADH + NH_2OH} \] \[ \ce{NH_2OH + O_2 ->[\text{HAO}] NO_2^- + H^+ + H_2O} \] \[ \ce{NH_2OH + 2Q + H_2O ->[\text{HAO}] NO_2^- + 2QH_2} \] \[ \ce{QH_2 ->[\text{UqO}] 2H^+ + Q} \] With the new model containing 1555 reactions and 1559 metabolites (hydroxylamine, Q and QH2 added), the optimal biomass flux of the modified model was found to be \( \boxed{228.6980~(\text{g dry weight}\cdot\text{h})^{-1}} \).

Results and Discussion

Because the modified biomass flux is greater than the original flux by nearly 2%, we can conclude that the cell will employ the inserted nitrification pathway in order to enhance its metabolism. [exchange flux vector spiel here] Thus, our device confers fitness advantage. One possible implication is that it is possible that our synthetic strain will out-compete the native strain inside the sludge, thus eliminating the need to artificially sustain the new culture. As was stated earlier, the expected cell phenotype is the one with the largest biomass flux. This is only true under the assumption that the cell lives in an ideal or near-ideal environment with no over-population. Several studies have shown that under different growth environments, cells sometimes exhibit non-optimal yield metabolism. We do not know whether wastewater causes a similar shift in metabolism of Paracoccus denitrificans. However, seeing as it is able to thrive in such environment gives enough reason to believe that the growth rate will not lose correlation with biomass flux.

References

[?] Oberhardt M., Chavali A., Papin J. (2009) Flux Balance Analysis: Interrogating Genome-Scale Metabolic Networks. In: Maly I. (eds) Systems Biology. Methods in Molecular Biology (Methods and Protocols), vol 500. Humana Press
[1] Feist, Adam M., and Bernhard O. Palsson. “The Biomass Objective Function.” Current opinion in microbiology 13.3 (2010): 344–349. PMC. Web. 28 July 2017.
[2] Cuevas, Daniel A. et al. “From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.” Frontiers in Microbiology 7 (2016): 907. PMC. Web. 27 July 2017.
[3] Henry, C.S., DeJongh, M., Best, A.B., Frybarger, P.M., Linsay, B., and R.L. Stevens. High-throughput Generation and Optimization of Genome-scale Metabolic Models. Nature Biotechnology, (2010).
[4] COBRApy: COnstraints-Based Reconstruction and Analysis for Python.
[5] Mackinac: A bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models.
[?] MLA Adadi, Roi et al. “Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters.” Ed. Nathan D. Price. PLoS Computational Biology 8.7 (2012): e1002575. PMC. Web. 31 July 2017.
[?} Molenaar, Douwe et al. “Shifts in Growth Strategies Reflect Tradeoffs in Cellular Economics.” Molecular Systems Biology 5 (2009): 323. PMC. Web. 31 July 2017.