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AIC Seminar Series

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

Mario Latendresse and Peter KarpArtificial Intelligence Center, SRI International[Home Page]

Date:  2011-05-11 at 11:00

Location:  EK255 (SRI E Building)  (Directions)

   Abstract

We and others have recently obtained the result that it is possible to automatically infer a quantitative model of an organism’s metabolic network from the genome sequence of that organism.

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. There are numerous reasons that a model may not provide appropriate results or no result at all (i.e., an infeasible model). 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, or even a single critical reaction is missing, flux-balance analyses cannot be performed.

We have recently developed methods for generating FBA models from metabolic databases, and for guiding the user in correcting certain classes of infeasible FBA models. Together these methods greatly reduce the time required to obtain a working FBA model.

This software tool is based on Mixed Integer Linear Programming. It 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 tool 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. Therefore, the method identifies new reactions to add to the model, it identifies minimal sets of required nutrients, and it identifies the maximal set of biomass components that can be produced by the completed model. Various parameters are provided to meet other scenarios.

   Bio for Mario Latendresse and Peter Karp

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