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This content will become publicly available on May 23, 2016

Title: Mapping the landscape of metabolic goals of a cell

Here, 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.
 [1] ;  [1] ;  [2] ;  [1] ;  [1]
  1. Boston Univ., Boston, MA (United States)
  2. Boston Univ., Boston, MA (United States); Memorial Sloan Kettering Cancer Center, New York, NY (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Genome Biology (Online)
Additional Journal Information:
Journal Name: Genome Biology (Online); Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1474-760X
BioMed Central
Research Org:
Boston Univ., MA (United States)
Sponsoring Org:
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES; metabolic networks; flux balance analysis; inverse optimization; objective functions; genome-scale stoichiometric models