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Title: Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Abstract

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis ( 13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S- 13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth ratemore » and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S- 13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.« less

Authors:
 [1];  [2];  [3];  [4];  [2]
  1. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Agile BioFoundry, Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division; Univ. of California, Berkeley, CA (United States). QB3 Inst.
  2. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Agile BioFoundry, Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division
  3. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division; Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering. Dept. of Computer Science
  4. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division; Univ. of California, Berkeley, CA (United States). QB3 Inst. Dept. of Bioengineering. Dept. of Chemical and Biomolecular Engineering; Technical Univ. of Denmark, Lyngby (Denmark). Novo Nordisk Foundation Center for Biosustainability
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Agile BioFoundry, Emeryville, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B); USDOE Office of Science (SC)
OSTI Identifier:
1506297
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Metabolites
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2218-1989
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; genome scale models; 13C metabolic flux analysis; two-scale 13C metabolic flux analysis; flux balance analysis; stoichiometry; linear programming; cellular metabolism; simulated annealing

Citation Formats

Backman, Tyler, Ando, David, Singh, Jahnavi, Keasling, Jay, and García Martín, Héctor. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis. United States: N. p., 2018. Web. doi:10.3390/metabo8010003.
Backman, Tyler, Ando, David, Singh, Jahnavi, Keasling, Jay, & García Martín, Héctor. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis. United States. doi:10.3390/metabo8010003.
Backman, Tyler, Ando, David, Singh, Jahnavi, Keasling, Jay, and García Martín, Héctor. Thu . "Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis". United States. doi:10.3390/metabo8010003. https://www.osti.gov/servlets/purl/1506297.
@article{osti_1506297,
title = {Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis},
author = {Backman, Tyler and Ando, David and Singh, Jahnavi and Keasling, Jay and García Martín, Héctor},
abstractNote = {Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.},
doi = {10.3390/metabo8010003},
journal = {Metabolites},
number = 1,
volume = 8,
place = {United States},
year = {2018},
month = {1}
}

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