Metabolic modeling for cell factory engineering: Glossary

load_model can be used to load a model.

model.solve() simulates the model.

reaction.flux contains the computed flux of the reaction.

model.solve() also returns a solution object that contains the simulation results.

model.metabolites contains all metabolites in model.

model.reactions contains all reactions in model.

model.genes contains all genes in model.

https://escher.github.io/ provides a standalone web application for pathway visualizations.

escher.Builder embeds pathway visualizations inside jupyter notebooks.

escher.Builder has keyword arguments for providing reaction, metabolite, and gene related data.

flux_variability_analysis calculates all the minimum and maximum fluxes that all reactions in a model can attain.

flux_variability_analysis has a keyword argument fraction_of_optimum that allows one to specify a fraction of the model’s objective that needs to be achieved.

cameo.util.TimeMachine provides a convenient way to undo changes to models in order to avoid copies.

reaction.change_bounds allows one to change the lower and upper bound of reaction simultaneously.

Reaction and Metabolite can be used to define reactions.

model.add_reaction(reaction) adds reaction to model.

model.genes.gene.knock_out deletes the gene from the model removing also all reactions affected by the knockout.

Set the exchange reaction of the product as the model objective using model.objective.

Optimize the model and divide the product flux by the carbon source uptake flux to determine the molar yield.

cameo.strain_design.PathwayPredictor computes shortest heterologous pathways for a desired product and host organism.

cameo.strain_design.OptGene is strain design methods that utilizes genetic algorithms to determine gene (or reaction) knockout combinations that are likely to increase production.

cameo.strain_design.OptKnock use mixed integer linear programming to compute knockout combinations that couple production to growth.

cameo.strain_design.DifferentialFVA uses flux variability analysis to compare flux ranges of reactions in production state vs. wild type state to determine which fluxes need to go up or down.

Ideally, computer-aided design software would aid bench biologists in cell factory engineering. Existing software is still not on par with programming libraries such as cameo and cobrapy in terms of features and flexibility.