title: Flux balance analysis: a geometric perspective.
abstract: Advances in the field of bioinformatics have led to reconstruction of genome-scale networks for a number of key organisms. The application of physicochemical constraints to these stoichiometric networks allows researchers, through methods such as flux balance analysis, to highlight key sets of reactions necessary to achieve particular objectives. The key benefits of constraint-based analysis lie in the minimal knowledge required to infer systemic properties. However, network degeneracy leads to a large number of flux distributions that satisfy any objective; moreover, these distributions may be dominated by biologically irrelevant internal cycles. By examining the geometry underlying the problem, we define two methods for finding a unique solution within the space of all possible flux distributions; such a solution contains no internal cycles, and is representative of the space as a whole. The first method draws on typical geometric knowledge, but cannot be applied to large networks because of the high computational complexity of the problem. Thus a second method, an iteration of linear programs which scales easily to the genome scale, is defined. The algorithm is run on four recent genome-scale models, and unique flux solutions are found. The algorithm set out here will allow researchers in flux balance analysis to exchange typical solutions to their models in a reproducible format. Moreover, having found a single solution, statistical analyses such as correlations may be performed.
authors.affiliation: Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester M1 7DN, UK. kieran.smallbone@manchester.ac.uk
authors.forename: Kieran
authors.initials: K
authors.lastname: Smallbone
authors.forename: Evangelos
authors.initials: E
authors.lastname: Simeonidis
mesh_terms: D000465:Algorithms; D000818:Animals; D053858:Metabolic Networks and Pathways; D055442:Metabolome; D008954:Models, Biological; D016571:Neural Networks, Computer; D012441:Saccharomyces cerevisiae / Q000378:metabolism