Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail) to eliminate them. As test cases, we tackle (a) the problem of identifying infeasible cycles in the E. coli metabolic network and (b) the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type-specific metabolic networks. Results for (a) are compared with previous analyses of the same issue, while results for (b) are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method, on the one hand, outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.

Counting and Correcting Thermodynamically Infeasible Flux Cycles in Genome-Scale Metabolic Networks / Daniele De, Martino; Capuani, Fabrizio; Mori, Matteo; Andrea De, Martino; Marinari, Vincenzo. - In: METABOLITES. - ISSN 2218-1989. - 3:4(2013), pp. 946-966. [10.3390/metabo3040946]

Counting and Correcting Thermodynamically Infeasible Flux Cycles in Genome-Scale Metabolic Networks

CAPUANI, FABRIZIO;MORI, MATTEO;MARINARI, Vincenzo
2013

Abstract

Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail) to eliminate them. As test cases, we tackle (a) the problem of identifying infeasible cycles in the E. coli metabolic network and (b) the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type-specific metabolic networks. Results for (a) are compared with previous analyses of the same issue, while results for (b) are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method, on the one hand, outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.
2013
01 Pubblicazione su rivista::01a Articolo in rivista
Counting and Correcting Thermodynamically Infeasible Flux Cycles in Genome-Scale Metabolic Networks / Daniele De, Martino; Capuani, Fabrizio; Mori, Matteo; Andrea De, Martino; Marinari, Vincenzo. - In: METABOLITES. - ISSN 2218-1989. - 3:4(2013), pp. 946-966. [10.3390/metabo3040946]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/540869
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