Difference between revisions of "Team:WashU StLouis/Modeling"

What is a genome-scale metabolic model?

A genome-scale metabolic model (GSM) is a network reconstruction of all known metabolic reactions in an organism and the genes that encode each associated enzyme.1 Through the construction of these networks, we can assess the metabolic capabilities of an organism as well as its potential to produce biologically useful metabolites.

What are the components of a genome-scale model

• Gene-Protein-Reaction (GPR) relationships
• Set of known metabolic reactions represented in a stoichiometric matrix. Identifies stoichiometric coefficients and directionality associated with each reaction.
• Scaled biomass equation. Assumes flux through the biomass equation is equal to the exponential growth rate of the organism. This is made from experimental measurements of biomass components.

You can download our GSM, adapted from a published model of E. coli strain DH10B (Monk et al)^[2], of nitrogen-fixing E. coli (strain WM1788) here.

Our modeling hypothesis:

How can we use genome scale modeling and flux balance analysis to optimize nitrogenase activity in vivo?

The key limiting factors of the nitrogenase reaction are ATP and reduced flavodoxin. Therefore, our analyses focused on ways to either produce or provide more of these essential metabolites to the nitrogen fixing cell.

• Looking for potential gene overexpressions which lead to increased production of intracellular reduced flavodoxin and ATP
• Looking for potential gene knockouts which redistribute flux, decreasing ATP consumption or leading to increased production of reduced flavodoxin
• Determining which supplemental metabolites lead to the largest increases in reduced flavodoxin and ATP production

Result: Pyruvate Metabolism

When we compared flux data for our wild type and nitrogen fixing WM1788 models, we found that pyruvate synthase had higher (more positive) flux ranges for the nitrogen fixing strain than for the wild type.

2 flavodoxin (semi-oxidized) + 1 pyruvate +1 coenzyme A ↔ 1 CO2 + 2 reduced flavodoxin + 1 H+ + 1 acetyl-coA

1 pyruvate + 1 coenzyme A + 1 NAD → 1 CO2 + 1 NADH + 1 acetyl-coA

1 pyruvate + 1 coenzyme A → 1 acetyl-co-A + 1 formate

Therefore, we determined that knocking out pyruvate dehydrogenase (see figure above) redistributed more flux through pyruvate synthase, increasing the positive effect and producing more reduced flavodoxin.

However, experimentally, pyruvate dehydrogenase deficiency in E. coli is associated with a buildup of pyruvate^[5], which is then converted into lactic acid, and results in compromised cell activity. To bypass this effect, we proposed simultaneous overexpression of pyruvate synthase, which will favorably produce more reduced flavodoxin from excess pyruvate.

Additional Work: Single and Double Gene Knockouts

When a gene is knocked out in a cell, a metabolic flux redistribution can result.

A wild type cell, with normal flux distribution.

Flux is redistributed in the mutant cell.

Thus, our goal in performing knockouts is to take advantage of flux redistributions, directing metabolic flux through pathways which produce more of the metabolites we desire. Optimally, we aim to couple metabolite production with biomass -- that is, in order to grow at all, the cell will need to produce more of the specified metabolite.

Nitrogen fixing E. coli is already ATP-limited (the cell has already redistributed flux as much as it can to produce the most ATP). Therefore, we focused most on looking for knockouts that couple reduced flavodoxin production to biomass.

The E. coli genome.^[6]

By knocking out every gene in our model (there are 1367), as well as performing all non-essential double gene knockouts (a total of 1,236,544 possible combinations), we can comprehensively assess the effect any single or double gene knockout has on intracellular biomass, ATP, and flavodoxin production. Unfortunately, while we did discover knockouts where maximum flavodoxin increased due to compromised biomass, we did not find any instances of coupling between flavodoxin and biomass.

Our Python workflow for assessing single gene knockouts can be found below

1. Create a list of all genes, constructed from the provided GPRs in the genome scale model.
2. Create lists of reactions that will be deleted for each gene knockout. This is done by evaluating each reaction GPR for each gene. If a gene is essential to a given GPR, the reaction is added to the list of reactions which will be deleted for that knockout.
3. Given a list of deleted reactions for each gene, FBA is run iteratively for each gene knockout. Running FBA will determine maximum biomass, max and min ATP values, and max and min flavodoxin values.
4. The FBA results are collected and outputted for further analysis.

What can we learn in the future?

The remaining questions:

• How can we search for new ways to couple reduced flavodoxin production to growth? Is it possible to do this?
  • Will require a reexamination of the reactions flavodoxin is involved in, their roles in metabolism, and the essentiality of parallel pathways.
• How can we extend our analyses of supplemental metabolites, in order to optimize cell energy and growth?
  • A possible approach to this problem involves extending our analysis from simple metabolites to more complex cell media. Given a database of available media, or a way to create our own media, we could provide a comprehensive formula for lab use that is optimized to provide the most energy to the cell.