Journal
GENOME BIOLOGY
Volume 17, Issue -, Pages -Publisher
BIOMED CENTRAL LTD
DOI: 10.1186/s13059-016-0968-2
Keywords
Metabolic networks; Flux balance analysis; Inverse optimization; Objective functions; Genome-scale stoichiometric models
Funding
- NSF [CNS-1239021, CCF-1527292, IIS-1237022, DEB- 1457695]
- ARO [W911NF-12-1-0390, W911NF-11-1-0227]
- US Department of Energy [DE-SC0012627]
- NIH [5R01GM089978, R01GM103502, 5R01DE024468, R01-GM093147]
- DARPA [HR0011-15-C-0091]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1237022] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1239021] Funding Source: National Science Foundation
- U.S. Department of Energy (DOE) [DE-SC0012627] Funding Source: U.S. Department of Energy (DOE)
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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.
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