New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an "ensemble averaging" procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws.

Constrained Allocation Flux Balance Analysis / Mori, Matteo; Hwa, Terence; Martin, Olivier C.; De Martino, Andrea; Marinari, Vincenzo. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-734X. - STAMPA. - 12:6(2016), p. e1004913. [10.1371/journal.pcbi.1004913]

Constrained Allocation Flux Balance Analysis

MORI, MATTEO;MARINARI, Vincenzo
2016

Abstract

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an "ensemble averaging" procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws.
2016
Computational Theory and Mathematics; Modeling and Simulation; Ecology, Evolution, Behavior and Systematics; Genetics; Molecular Biology; Ecology; Cellular and Molecular Neuroscience
01 Pubblicazione su rivista::01a Articolo in rivista
Constrained Allocation Flux Balance Analysis / Mori, Matteo; Hwa, Terence; Martin, Olivier C.; De Martino, Andrea; Marinari, Vincenzo. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-734X. - STAMPA. - 12:6(2016), p. e1004913. [10.1371/journal.pcbi.1004913]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/884389
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