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Title: 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 » and in developing new, efficient strain designs.« less

Authors:
ORCiD logo [1];  [2];  [2]; ORCiD logo [2]; ORCiD logo [1];  [3]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Northwestern Univ., Evanston, IL (United States)
  3. The Pennsylvania State Univ., University Park, PA (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
OSTI Identifier:
1576471
Alternate Identifier(s):
OSTI ID: 1573099
Report Number(s):
NREL/JA-2700-75513
Journal ID: ISSN 1553-7358
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
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
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, 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, Costas D. Bayesian inference of metabolic kinetics from genome-scale multiomics data. United States. doi:10.1371/journal.pcbi.1007424.
St. John, Peter C., Strutz, Jonathan, Broadbelt, Linda J., Tyo, Keith E. J., Bomble, Yannick J., and Maranas, Costas D. Mon . "Bayesian inference of metabolic kinetics from genome-scale multiomics data". United States. doi:10.1371/journal.pcbi.1007424. https://www.osti.gov/servlets/purl/1576471.
@article{osti_1576471,
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, 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 = {2019},
month = {11}
}

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