S89: Multiple Gap-Filling to Speed Generation of Flux-Balance Models

Flux Balance Analysis (FBA) can be applied to metabolic models to predict the growth rate of an organism, analyze the effect of gene deletions, and more. But obtaining a working FBA model can be challenging and time consuming. Indeed, a workable FBA model is based on: 1) A sufficiently rich set of balanced reactions; 2) a correct set of biomass  metabolites; 3) an appropriate set of secreted metabolites; and 4) a sufficient set of nutrient compounds.  If only one of these requirements is not met, flux-balance analysis cannot be done.  We present a method for generating FBA models from metabolic databases, and for guiding the user in correcting certain classes of infeasible FBA models. This method greatly reduces the time required to get a working FBA model.

This method, based on Mixed Integer Linear Programming, obtains a working FBA model using a multiple gap-filling approach.  Starting from a possibly incomplete set of reactions, nutrients, secretions, and biomass metabolites, multiple gap-filling completes these sets to obtain a feasible FBA model by adding new reactions from a reaction database and new secretions, nutrients, and biomass metabolites from user provided ``try-sets''.

In a typical scenario, a user provides a base set of reactions for the organism, and try-sets for the biomass, secretions, and nutrients. The method adds as many metabolites as possible from the biomass try-set using a minimum number of added reactions, nutrients, and secretions of the try-sets to get a workable FBA model. This method has been
integrated into Pathway Tools.