Imperial College London
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DrJohnPinney

Central Faculty, Graduate School

Teaching Fellow- Data Science Skills Leader
 
 
 
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Contact

 

+44 (0)20 7594 8629j.pinney

 
 
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Location

 

327Sherfield BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zurauskiene:2014:bioinformatics/btu069,
author = {Zurauskiene, J and Kirk, P and Thorne, T and Pinney, J and Stumpf, M},
doi = {bioinformatics/btu069},
journal = {Bioinformatics},
pages = {1892--1898},
title = {Derivative processes for modelling metabolic fluxes},
url = {http://dx.doi.org/10.1093/bioinformatics/btu069},
volume = {30},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Zurauskiene,J
AU  - Kirk,P
AU  - Thorne,T
AU  - Pinney,J
AU  - Stumpf,M
DO  - bioinformatics/btu069
EP  - 1898
PY  - 2014///
SN  - 1367-4803
SP  - 1892
TI  - Derivative processes for modelling metabolic fluxes
T2  - Bioinformatics
UR  - http://dx.doi.org/10.1093/bioinformatics/btu069
UR  - http://hdl.handle.net/10044/1/39921
VL  - 30
ER  -
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