Approximate parameter inference in systems biology using gradient matching: a comparative evaluation

Macdonald, B., Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Approximate parameter inference in systems biology using gradient matching: a comparative evaluation. BioMedical Engineering OnLine, 15, 80. (doi:10.1186/s12938-016-0186-x) (PMID:27454253) (PMCID:PMC4959362)

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Abstract

Background: A challenging problem in current systems biology is that of parameter inference in biological pathways expressed as coupled ordinary differential equations (ODEs). Conventional methods that repeatedly numerically solve the ODEs have large associated computational costs. Aimed at reducing this cost, new concepts using gradient matching have been proposed, which bypass the need for numerical integration. This paper presents a recently established adaptive gradient matching approach, using Gaussian processes, combined with a parallel tempering scheme, and conducts a comparative evaluation with current state of the art methods used for parameter inference in ODEs. Among these contemporary methods is a technique based on reproducing kernel Hilbert spaces (RKHS). This has previously shown promising results for parameter estimation, but under lax experimental settings. We look at a range of scenarios to test the robustness of this method. We also change the approach of inferring the penalty parameter from AIC to cross validation to improve the stability of the method. Methodology: Methodology for the recently proposed adaptive gradient matching method using Gaussian processes, upon which we build our new method, is provided. Details of a competing method using reproducing kernel Hilbert spaces are also described here. Results: We conduct a comparative analysis for the methods described in this paper, using two benchmark ODE systems. The analyses are repeated under different experimental settings, to observe the sensitivity of the techniques. Conclusions: Our study reveals that for known noise variance, our proposed method based on Gaussian processes and parallel tempering achieves overall the best performance. When the noise variance is unknown, the RKHS method proves to be more robust.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Filippone, Dr Maurizio and Husmeier, Professor Dirk and Niu, Dr Mu and Macdonald, Dr Benn and Rogers, Dr Simon
Authors: Macdonald, B., Niu, M., Rogers, S., Filippone, M., and Husmeier, D.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:BioMedical Engineering OnLine
Publisher:Biomed Central
ISSN:1475-925X
ISSN (Online):1475-925X
Published Online:15 July 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in BioMedical Engineering OnLine 15:80
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.5525/gla.researchdata.288

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering & Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS