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research article

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

Miskovic, Ljubisa  
•
Béal, Jonas
•
Moret, Michael
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August 20, 2019
PLoS Computational Biology

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.

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Type
research article
DOI
10.1371/journal.pcbi.1007242
Author(s)
Miskovic, Ljubisa  
Béal, Jonas
Moret, Michael
Hatzimanikatis, Vassily  
Date Issued

2019-08-20

Published in
PLoS Computational Biology
Volume

15

Issue

8

Article Number

e1007242

Subjects

kinetic models

•

machine learning

•

ORACLE

•

iSCHRUNK

•

Uncertainty

•

Metabolic control analysis

•

Metabolism

•

Saccharomyces cerevisiae

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LCSB  
FunderGrant Number

FNS

2015/313

FNS

315230_163423

RelationURL/DOI

IsSupplementedBy

https://doi.org/10.5281/zenodo.3240300
Available on Infoscience
August 30, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/160732
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