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Open AccessArticle

A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models

Department d'Enginyeria Química, Universitat Rovira i Virgili, Tarragona 43007, Spain
Centre de Recerca Matemàtica, Edifici C, Campus Bellaterra, Bellaterra (Barcelona) E-08193, Spain
Department de Matemàtiques, Universitat Autónoma de Barcelona, Edifici C, Bellaterra (Barcelona) E-08193, Spain
Dipartimento di Fisica, Sapienza Università di Roma, p. A. Moro 2, Rome 00185, Italy
CNR/IPCF, UOS di Roma, Roma 00185, Italy
Center for Life Nano [email protected], Istituto Italiano di Tecnologia, v. Regina Elena 291, Rome 00161, Italy
Department of Mathematics, King's College London, London WC2R 2LS, UK
Author to whom correspondence should be addressed.
Metabolites 2013, 3(3), 838-852;
Received: 1 August 2013 / Revised: 10 September 2013 / Accepted: 10 September 2013 / Published: 20 September 2013
(This article belongs to the Special Issue Data Processing in Metabolomics)
Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production and consumption fluxes for each chemical species in the system. In some cases, the complexity of the solution space can be reduced by performing an additional optimization, while in other cases, knowing the range of variability of fluxes over the polytope provides a sufficient characterization of the allowed configurations. There are cases, however, in which the thorough information encoded in the individual distributions of viable fluxes over the polytope is required. Obtaining such distributions is known to be a highly challenging computational task when the dimensionality of the polytope is sufficiently large, and the problem of developing cost-effective ad hoc algorithms has recently seen a major surge of interest. Here, we propose a method that allows us to perform the required computation heuristically in a time scaling linearly with the number of reactions in the network, overcoming some limitations of similar techniques employed in recent years. As a case study, we apply it to the analysis of the human red blood cell metabolic network, whose solution space can be sampled by different exact techniques, like Hit-and-Run Monte Carlo (scaling roughly like the third power of the system size). Remarkably accurate estimates for the true distributions of viable reaction fluxes are obtained, suggesting that, although further improvements are desirable, our method enhances our ability to analyze the space of allowed configurations for large biochemical reaction networks. View Full-Text
Keywords: metabolic networks; flux balance analysis; belief propagation algorithm metabolic networks; flux balance analysis; belief propagation algorithm
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Massucci, F.A.; Font-Clos, F.; De Martino, A.; Castillo, I.P. A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models. Metabolites 2013, 3, 838-852.

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