skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Mapping the landscape of metabolic goals of a cell

Abstract

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.

Authors:
 [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:
Research Org.:
Boston Univ., MA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1310268
Grant/Contract Number:  
SC0012627
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Genome Biology (Online)
Additional Journal Information:
Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1474-760X
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; metabolic networks; flux balance analysis; inverse optimization; objective functions; genome-scale stoichiometric models

Citation Formats

Zhao, Qi, Stettner, Arion I., Reznik, Ed, Paschalidis, Ioannis Ch., and Segre, Daniel. Mapping the landscape of metabolic goals of a cell. United States: N. p., 2016. Web. doi:10.1186/s13059-016-0968-2.
Zhao, Qi, Stettner, Arion I., Reznik, Ed, Paschalidis, Ioannis Ch., & Segre, Daniel. Mapping the landscape of metabolic goals of a cell. United States. doi:10.1186/s13059-016-0968-2.
Zhao, Qi, Stettner, Arion I., Reznik, Ed, Paschalidis, Ioannis Ch., and Segre, Daniel. Mon . "Mapping the landscape of metabolic goals of a cell". United States. doi:10.1186/s13059-016-0968-2. https://www.osti.gov/servlets/purl/1310268.
@article{osti_1310268,
title = {Mapping the landscape of metabolic goals of a cell},
author = {Zhao, Qi and Stettner, Arion I. and Reznik, Ed and Paschalidis, Ioannis Ch. and Segre, Daniel},
abstractNote = {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.},
doi = {10.1186/s13059-016-0968-2},
journal = {Genome Biology (Online)},
issn = {1474-760X},
number = 1,
volume = 17,
place = {United States},
year = {2016},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

Figures / Tables:

Fig. 1 Fig. 1: Schematic representation of how FBA and invFBA work. This diagram illustrates concisely the flow of information for invFBA calculations in this work. The right part of the figure displays schematic representations of the set of metabolic fluxes. Each flux vector can also be visualized on a metabolic chartmore » (right-most part of the figure), where gray arrows of different thicknesses indicate different intensities of reaction fluxes throughout a network. The left part of the figure displays instead the space of metabolic objectives. Coefficients of the objective function can also be visualized on a metabolic chart (left-most part of the figure), with red arrows representing non-zero components of the objective. a FBA uses a given objective function (here cgrowth) to predict a set of fluxes (XOpt), or multiple equivalent sets of fluxes (not shown). From one FBA solution, one can use invFBA to infer possible objective functions. The solution is not necessarily unique, though the space of possible solutions can be rigorously characterized, and contains the original objective function. b InvFBA can be applied to multiple (noisy) experimental measurements of fluxes, leading, as in the test case of (a), to a space of possible objective functions« less

Save / Share:

Works referenced in this record:

Effect of Escherichia coli biomass composition on central metabolic fluxes predicted by a stoichiometric model
journal, October 1998


    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.