Mapping the landscape of metabolic goals of a cell
Files
Published version
Date
2016-05
Authors
Segrè, Daniel
Paschalidis, Ioannis Ch.
Zhao, Qi
Stettner, Arion I.
Reznik, Ed
Version
OA Version
Citation
Qi Zhao, Arion I Stettner, Ed Reznik, Ioannis Ch Paschalidis, Daniel Segrè. 2016. "Mapping the landscape of metabolic goals of a cell." Genome Biology, Volume 17, Issue 1.
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.
Description
License
Attribution 4.0 International