From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline
Abstract
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis ( 13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting withinmore »
- Authors:
-
[1];
[1]
- Pennsylvania State University, University Park, PA (United States)
- University of Delaware, Newark, DE (United States)
- Publication Date:
- Research Org.:
- Pennsylvania State Univ., University Park, PA (United States); Univ. of Delaware, Newark, DE (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1903904
- Grant/Contract Number:
- AC05-00OR22725; MCB-1615646
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online); Journal Volume: 15; Journal Issue: 9; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; metabolic networks; metabolites; metabolic labeling; metabolic pathways; enzyme metabolism; glucose; genetics; mutant strains
Citation Formats
Foster, Charles J., Gopalakrishnan, Saratram, Antoniewicz, Maciek R., and Maranas, Costas D. From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. United States: N. p., 2019.
Web. doi:10.1371/journal.pcbi.1007319.
Foster, Charles J., Gopalakrishnan, Saratram, Antoniewicz, Maciek R., & Maranas, Costas D. From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. United States. https://doi.org/10.1371/journal.pcbi.1007319
Foster, Charles J., Gopalakrishnan, Saratram, Antoniewicz, Maciek R., and Maranas, Costas D. Tue .
"From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline". United States. https://doi.org/10.1371/journal.pcbi.1007319. https://www.osti.gov/servlets/purl/1903904.
@article{osti_1903904,
title = {From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline},
author = {Foster, Charles J. and Gopalakrishnan, Saratram and Antoniewicz, Maciek R. and Maranas, Costas D.},
abstractNote = {Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis ( 13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.},
doi = {10.1371/journal.pcbi.1007319},
journal = {PLoS Computational Biology (Online)},
number = 9,
volume = 15,
place = {United States},
year = {Tue Sep 10 00:00:00 EDT 2019},
month = {Tue Sep 10 00:00:00 EDT 2019}
}
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Analysis of Carbon Metabolism in Escherichia coli Strains with an Inactive Phosphotransferase System by 13C Labeling and NMR Spectroscopy
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journal, April 2002
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journal, October 2015
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journal, January 2015
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Metabolic flux analysis in eukaryotes
journal, February 2010
- Niklas, Jens; Schneider, Konstantin; Heinzle, Elmar
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Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook
journal, August 2014
- Long, Christopher P.; Antoniewicz, Maciek R.
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Advances in analytical tools for high throughput strain engineering
journal, December 2018
- Marcellin, Esteban; Nielsen, Lars Keld
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A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes
journal, July 2013
- Smallbone, Kieran; Messiha, Hanan L.; Carroll, Kathleen M.
- FEBS Letters, Vol. 587, Issue 17
Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures
journal, February 2018
- Kurata, Hiroyuki; Sugimoto, Yurie
- Journal of Bioscience and Bioengineering, Vol. 125, Issue 2
Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments
journal, April 2007
- Nöh, Katharina; Grönke, Karsten; Luo, Bing
- Journal of Biotechnology, Vol. 129, Issue 2
Evaluation of 13C isotopic tracers for metabolic flux analysis in mammalian cells
journal, November 2009
- Metallo, Christian M.; Walther, Jason L.; Stephanopoulos, Gregory
- Journal of Biotechnology, Vol. 144, Issue 3
Optimal re-design of primary metabolism in Escherichia coli using linlog kinetics
journal, October 2004
- Visser, Diana; Schmid, Joachim W.; Mauch, Klaus
- Metabolic Engineering, Vol. 6, Issue 4
Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements
journal, July 2006
- Antoniewicz, Maciek R.; Kelleher, Joanne K.; Stephanopoulos, Gregory
- Metabolic Engineering, Vol. 8, Issue 4
Elementary metabolite units (EMU): A novel framework for modeling isotopic distributions
journal, January 2007
- Antoniewicz, Maciek R.; Kelleher, Joanne K.; Stephanopoulos, Gregory
- Metabolic Engineering, Vol. 9, Issue 1
Metabolic flux analysis in mammalian cell culture
journal, March 2010
- Quek, Lake-Ee; Dietmair, Stefanie; Krömer, Jens O.
- Metabolic Engineering, Vol. 12, Issue 2
Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA
journal, September 2011
- Xu, Peng; Ranganathan, Sridhar; Fowler, Zachary L.
- Metabolic Engineering, Vol. 13, Issue 5
Parallel labeling experiments with [U-13C]glucose validate E. coli metabolic network model for 13C metabolic flux analysis
journal, September 2012
- Leighty, Robert W.; Antoniewicz, Maciek R.
- Metabolic Engineering, Vol. 14, Issue 5
COMPLETE-MFA: Complementary parallel labeling experiments technique for metabolic flux analysis
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