RESUMEN
Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present GlobalFit, a bi-level optimization method that finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%.
Asunto(s)
Algoritmos , Biología Computacional/métodos , Escherichia coli/metabolismo , Técnicas de Inactivación de Genes , Redes y Vías Metabólicas , Mycoplasma genitalium/metabolismo , Simulación por Computador , Escherichia coli/genética , Redes y Vías Metabólicas/genética , Redes y Vías Metabólicas/fisiología , Modelos Biológicos , Mycoplasma genitalium/genéticaRESUMEN
MOTIVATION: Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict metabolic phenotypes, e.g. growth rates, ATP yield or the fitness of gene knockouts. One frequent difficulty of constraint-based solutions is the inclusion of thermodynamically infeasible loops (or internal cycles), which add nonbiological fluxes to the predictions. RESULTS: We propose a simple postprocessing of constraint-based solutions, which removes internal cycles from any given flux distribution [Formula: see text] without disturbing other fluxes not involved in the loops. This new algorithm, termed CycleFreeFlux, works by minimizing the sum of absolute fluxes [Formula: see text] while (i) conserving the exchange fluxes and (ii) using the fluxes of the original solution to bound the new flux distribution. This strategy reduces internal fluxes until at least one reaction of every possible internal cycle is inactive, a necessary and sufficient condition for the thermodynamic feasibility of a flux distribution. If alternative representations of the input flux distribution in terms of elementary flux modes exist that differ in their inclusion of internal cycles, then CycleFreeFlux is biased towards solutions that maintain the direction given by [Formula: see text] and towards solutions with lower total flux [Formula: see text]. Our method requires only one additional linear optimization, making it computationally very efficient compared to alternative strategies. AVAILABILITY AND IMPLEMENTATION: We provide freely available R implementations for the enumeration of thermodynamically infeasible cycles as well as for cycle-free FBA solutions, flux variability calculations and random sampling of solution spaces. CONTACT: lercher@cs.uni-duesseldorf.de.