Abstract

Citation

Researchers should cite this work as follows:

• Brewer, D. M. (2023). 2008-Brewer-EtAl-Fitting ordinary differential equations to short time course data. SIMIODE, QUBES Educational Resources. doi:10.25334/XD8G-EW39

  BibTex | EndNote

Article Context

Description

Brewer, D., M. Barenco, R. Callars, M. Hubank, and J. Stak. 2008. Fitting ordinary differential equations to short time course data. Phil. Trans. R. Soc. A. 366: 519–544.

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.

The authors generated a toy set of data and described how they did so for a simple four-component model of the core of p53 gene regulatory network.

Keywords:  differential equation, system, model, parameter estimation, time series, spline, biology, gene