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Derivative processes for modelling metabolic fluxes

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Title: Derivative processes for modelling metabolic fluxes
Authors: Zurauskiene, J
Kirk, P
Thorne, T
Pinney, J
Stumpf, M
Item Type: Journal Article
Abstract: Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. Results: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.
Issue Date: 26-Feb-2014
Date of Acceptance: 26-Jan-2014
URI: http://hdl.handle.net/10044/1/39921
DOI: http://dx.doi.org/10.1093/bioinformatics/btu069
ISSN: 1367-4803
Publisher: Oxford University Press
Start Page: 1892
End Page: 1898
Journal / Book Title: Bioinformatics
Volume: 30
Issue: 13
Copyright Statement: © The Author 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: The Royal Society
Biotechnology and Biological Sciences Research Council (BBSRC)
The Leverhulme Trust
Human Frontier Science Program
Biotechnology and Biological Sciences Research Council (BBSRC)
Funder's Grant Number: 516002.K501/SC/PM/ROG
BB/G020434/1
F/07 058/BP
RGP0061/2011
BB/K003909/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
BIOCHEMICAL RESEARCH METHODS
BIOTECHNOLOGY & APPLIED MICROBIOLOGY
MATHEMATICAL & COMPUTATIONAL BIOLOGY
TIME-SERIES DATA
PATHWAY ANALYSIS
PROFILES
IDENTIFICATION
SYSTEMS
Bayes Theorem
Escherichia coli
Metabolic Networks and Pathways
Models, Biological
Nitrogen
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Publication Status: Published
Appears in Collections:Department of Medicine (up to 2019)
Faculty of Natural Sciences