Fitting ordinary differential equations to short time course data

Brewer, Daniel ORCID: https://orcid.org/0000-0003-4753-9794, Barenco, Martino, Callard, Robin, Hubank, Michael and Stark, Jaroslav (2008) Fitting ordinary differential equations to short time course data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366 (1865). pp. 519-544. ISSN 1364-503X

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Abstract

Ordinary differential equations (ODEs) are widely used to model many systems in physics, chemistry, engineering and biology. Often one wants to compare such equations with observed time course data, and use this to estimate parameters. Surprisingly, practical algorithms for doing this are relatively poorly developed, particularly in comparison with the sophistication of numerical methods for solving both initial and boundary value problems for differential equations, and for locating and analysing bifurcations. A lack of good numerical fitting methods is particularly problematic in the context of systems biology where only a handful of time points may be available. In this paper, we present a survey of existing algorithms and describe the main approaches. We also introduce and evaluate a new efficient technique for estimating ODEs linear in parameters particularly suited to situations where noise levels are high and the number of data points is low. It employs a spline-based collocation scheme and alternates linear least squares minimization steps with repeated estimates of the noise-free values of the variables. This is reminiscent of expectation-maximization methods widely used for problems with nuisance parameters or missing data.

Item Type:	Article
Uncontrolled Keywords:	algorithms,ataxia telangiectasia mutated proteins,cell cycle proteins,computational biology,dna-binding proteins,data interpretation, statistical,genes, p53,linear models,mathematics,models, biological,models, statistical,protein-serine-threonine kinases,proto-oncogene proteins c-mdm2,systems biology,time factors,tumor suppressor protein p53,tumor suppressor proteins
Faculty \ School:	Faculty of Science > School of Biological Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Cancer Studies Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Depositing User:	Pure Connector
Date Deposited:	06 Jan 2014 14:14
Last Modified:	19 Oct 2023 01:15
URI:	https://ueaeprints.uea.ac.uk/id/eprint/47039
DOI:	10.1098/rsta.2007.2108

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