k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design
Abstract
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. Amore »
- Authors:
-
- Pennsylvania State Univ., University Park, PA (United States). Dept. of Chemical Engineering
- Publication Date:
- Research Org.:
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE; National Science Foundation (NSF)
- OSTI Identifier:
- 1903940
- Grant/Contract Number:
- SC10822882; EEC-0813570
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online); Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Mathematical & Computational Biology
Citation Formats
Chowdhury, Anupam, Zomorrodi, Ali R., and Maranas, Costas D. k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design. United States: N. p., 2014.
Web. doi:10.1371/journal.pcbi.1003487.
Chowdhury, Anupam, Zomorrodi, Ali R., & Maranas, Costas D. k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design. United States. https://doi.org/10.1371/journal.pcbi.1003487
Chowdhury, Anupam, Zomorrodi, Ali R., and Maranas, Costas D. Thu .
"k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design". United States. https://doi.org/10.1371/journal.pcbi.1003487. https://www.osti.gov/servlets/purl/1903940.
@article{osti_1903940,
title = {k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design},
author = {Chowdhury, Anupam and Zomorrodi, Ali R. and Maranas, Costas D.},
abstractNote = {Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.},
doi = {10.1371/journal.pcbi.1003487},
journal = {PLoS Computational Biology (Online)},
number = 2,
volume = 10,
place = {United States},
year = {Thu Feb 20 00:00:00 EST 2014},
month = {Thu Feb 20 00:00:00 EST 2014}
}
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