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Open Access Highly Accessed Research article

Efficiently gap-filling reaction networks

Mario Latendresse

Author Affiliations

Bioinformatics Research Group/Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, Menlo Park 94025, USA

BMC Bioinformatics 2014, 15:225  doi:10.1186/1471-2105-15-225

Published: 28 June 2014

Abstract

Background

Flux Balance Analysis (FBA) is a genome-scale computational technique for modeling the steady-state fluxes of an organism’s reaction network. When the organism’s reaction network needs to be completed to obtain growth using FBA, without relying on the genome, the completion process is called reaction gap-filling. Currently, computational techniques used to gap-fill a reaction network compute the minimum set of reactions using Mixed-Integer Linear Programming (MILP). Depending on the number of candidate reactions used to complete the model, MILP can be computationally demanding.

Results

We present a computational technique, called FastGapFilling, that efficiently completes a reaction network by using only Linear Programming, not MILP. FastGapFilling creates a linear program with all candidate reactions, an objective function based on their weighted fluxes, and a variable weight on the biomass reaction: no integer variable is used. A binary search is performed by modifying the weight applied to the flux of the biomass reaction, and solving each corresponding linear program, to try reducing the number of candidate reactions to add to the network to generate a working model. We show that this method has proved effective on a series of incomplete E. coli and yeast models with, in some cases, a three orders of magnitude execution speedup compared with MILP. We have implemented FastGapFilling in MetaFlux as part of Pathway Tools (version 17.5), which is freely available to academic users, and for a fee to commercial users. Download from: biocyc.org/download.shtml.

Conclusions

The computational technique presented is very efficient allowing interactive completion of reaction networks of FBA models. Computational techniques based on MILP cannot offer such fast and interactive completion.

Keywords:
Flux Balance Analysis (FBA); Gap-filling; Systems biology; Reaction network; Linear Programming (LP); Mixed-Integer Linear Programming (MILP)