期刊
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
卷 366, 期 1865, 页码 519-544出版社
ROYAL SOC
DOI: 10.1098/rsta.2007.2108
关键词
parameter estimation; ordinary differential equation; time series; splines; collocation; systems biology
资金
- BBSRC [BB/E008488/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/E008488/1] Funding Source: researchfish
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.
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