Gradient matching methods for computational inference in mechanistic models for systems biology: a review and comparative analysis

Macdonald, B. and Husmeier, D. (2015) Gradient matching methods for computational inference in mechanistic models for systems biology: a review and comparative analysis. Frontiers in Bioengineering and Biotechnology, 3, 180. (doi:10.3389/fbioe.2015.00180) (PMID:26636071) (PMCID:PMC4654429)

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Abstract

Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem in contemporary systems biology. Conventional methods involve repeatedly solving the ODEs by numerical integration, which is computationally onerous and does not scale up to complex systems. Aimed at reducing the computational costs, new concepts based on gradient matching have recently been proposed in the computational statistics and machine learning literature. In a preliminary smoothing step, the time series data are interpolated; then, in a second step, the parameters of the ODEs are optimised so as to minimise some metric measuring the difference between the slopes of the tangents to the interpolants, and the time derivatives from the ODEs. In this way, the ODEs never have to be solved explicitly. This review provides a concise methodological overview of the current state-of-the-art methods for gradient matching in ODEs, followed by an empirical comparative evaluation based on a set of widely used and representative benchmark data.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Macdonald, Dr Benn
Authors: Macdonald, B., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Frontiers in Bioengineering and Biotechnology
Publisher:Frontiers
ISSN:2296-4185
ISSN (Online):2296-4185
Copyright Holders:Copyright © 2015 Macdonald and Husmeier.
First Published:First published in Front. Bioeng. Biotechnol. 3:180.

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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