Year: 2016
Type: article
Abstract: Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories.
Source: Genome biology
Institutions: Boston University, Memorial Sloan Kettering Cancer Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Awards: G234936229, G2515541506, G280295191, G3140331540, A HIGH-THROUGHPUT PIPELINE FOR MAPPING INTER-SPECIES INTERACTIONS AND METABOLIC SYNERGY RELEVANT TO NEXT-GENERATION BIOFUEL PRODUCTION, G4577129405, Computation and functional significance of multi-phenotype genetic interaction ma, G6377098844, Cultivation, Nature, Ecology and Pathogenicity of the Uncultivable Oral Microbiom, G7452299184, Visant-Predictome: A System for Integration, Mining, Visualization and Analysis, G748954555, Collaborative Research: Molecular mechanisms and biogeochemical consequences of decomposer species interactions during succession in ecosystems, SHB: Type II (INT): Collaborative Research: Algorithmic Approaches to Personalized Health Care, AF: Small: Manifold optimization algorithms for protein-protein docking, G8170182421, CPS: Synergy: Collaborative Research: A Cyber-Physical Infrastructure for the "Smart City", G844935001, G862988727, G9000133042
OpenAlex ID: W2394694036