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Derivative processes for modelling metabolic fluxes

Zurauskiene, Justina, Kirk, Paul, Thorne, Tom, Pinney, John and Stumpf, Michael (2014) Derivative processes for modelling metabolic fluxes Bioinformatics, 30 (13). pp. 1892-1898.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Zurauskiene, Justina
Kirk, Paul
Pinney, John
Stumpf, Michael
Date : 26 February 2014
Funders : Leverhulme Trust, Royal Society, BBSRC
DOI : 10.1093/bioinformatics/btu069
Copyright Disclaimer : � The Author 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Depositing User : James Marshall
Date Deposited : 17 Jun 2020 12:49
Last Modified : 17 Jun 2020 12:49

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