Multiple Gap-Filling to Speed Generation of Flux-Balance Models
|Mario Latendresse and Peter Karp||Artificial Intelligence Center, SRI International||[Home Page]|
Date: Wednesday, May 11th 2011 at 11:00am
Location: EK255 (SRI E Building) (Directions)
We and others have recently obtained the result that it is possible to automatically infer a quantitative model of an organisms 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.
Please arrive at least 10 minutes early as you will need to sign in by following instructions by the lobby phone at Building E. (or call Wilma Lenz at 650 859 4904, or Vicenta at Lopez at 650 859 5750). SRI is located at 333 Ravenswood Avenue in Menlo Park. Visitors may park in the parking lots off Fourth Street. Detailed directions to SRI, as well as maps, are available from the Visiting AIC web page. There are two entrances to SRI International located on Ravenswood Ave. Please check the Builing E entrance signage.