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Title: Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model

Abstract

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. Here, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properlymore » parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.« less

Authors:
 [1];  [1];  [1]
  1. Pennsylvania State Univ., University Park, PA (United States). Dept. of Chemical Engineering
Publication Date:
Research Org.:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1629802
Grant/Contract Number:  
SC0008091; EEC-0813570
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Bioengineering and Biotechnology
Additional Journal Information:
Journal Volume: 2; Journal ID: ISSN 2296-4185
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; computational strain design; kinetic model; bilevel optimization; succinate overproduction; model parameterization

Citation Formats

Khodayari, Ali, Chowdhury, Anupam, and Maranas, Costas D. Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model. United States: N. p., 2015. Web. doi:10.3389/fbioe.2014.00076.
Khodayari, Ali, Chowdhury, Anupam, & Maranas, Costas D. Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model. United States. https://doi.org/10.3389/fbioe.2014.00076
Khodayari, Ali, Chowdhury, Anupam, and Maranas, Costas D. Mon . "Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model". United States. https://doi.org/10.3389/fbioe.2014.00076. https://www.osti.gov/servlets/purl/1629802.
@article{osti_1629802,
title = {Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model},
author = {Khodayari, Ali and Chowdhury, Anupam and Maranas, Costas D.},
abstractNote = {Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. Here, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.},
doi = {10.3389/fbioe.2014.00076},
journal = {Frontiers in Bioengineering and Biotechnology},
number = ,
volume = 2,
place = {United States},
year = {Mon Jan 05 00:00:00 EST 2015},
month = {Mon Jan 05 00:00:00 EST 2015}
}

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