Bayesian inference of metabolic kinetics from genome-scale multiomics data
Abstract
Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological datamore »
- Authors:
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)
- OSTI Identifier:
- 1607549
- Alternate Identifier(s):
- OSTI ID: 1573099; OSTI ID: 1576471
- Report Number(s):
- NREL/JA-2700-75513
Journal ID: ISSN 1553-7358; 10.1371/journal.pcbi.1007424
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Published Article
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online) Journal Volume: 15 Journal Issue: 11; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science (PLoS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 09 BIOMASS FUELS; 59 BASIC BIOLOGICAL SCIENCES; enzyme metabolism; enzymes; metabolites; enzyme kinetics; enzyme regulation; lysine; pyruvate; protein metabolism
Citation Formats
St. John, Peter C., Strutz, Jonathan, Broadbelt, Linda J., Tyo, Keith E. J., Bomble, Yannick J., and Maranas, ed., Costas D. Bayesian inference of metabolic kinetics from genome-scale multiomics data. United States: N. p., 2019.
Web. doi:10.1371/journal.pcbi.1007424.
St. John, Peter C., Strutz, Jonathan, Broadbelt, Linda J., Tyo, Keith E. J., Bomble, Yannick J., & Maranas, ed., Costas D. Bayesian inference of metabolic kinetics from genome-scale multiomics data. United States. https://doi.org/10.1371/journal.pcbi.1007424
St. John, Peter C., Strutz, Jonathan, Broadbelt, Linda J., Tyo, Keith E. J., Bomble, Yannick J., and Maranas, ed., Costas D. Mon .
"Bayesian inference of metabolic kinetics from genome-scale multiomics data". United States. https://doi.org/10.1371/journal.pcbi.1007424.
@article{osti_1607549,
title = {Bayesian inference of metabolic kinetics from genome-scale multiomics data},
author = {St. John, Peter C. and Strutz, Jonathan and Broadbelt, Linda J. and Tyo, Keith E. J. and Bomble, Yannick J. and Maranas, ed., Costas D.},
abstractNote = {Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and in developing new, efficient strain designs.},
doi = {10.1371/journal.pcbi.1007424},
journal = {PLoS Computational Biology (Online)},
number = 11,
volume = 15,
place = {United States},
year = {Mon Nov 04 00:00:00 EST 2019},
month = {Mon Nov 04 00:00:00 EST 2019}
}
https://doi.org/10.1371/journal.pcbi.1007424
Web of Science
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