Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 9, Issue 73, Pages 1925-1933Publisher
ROYAL SOC
DOI: 10.1098/rsif.2011.0767
Keywords
parameter fitting; optimization; evolutionary algorithms; ordinary differential equation models; Monte Carlo methods
Categories
Funding
- US Office of Naval Research
- BBSRC through LoLa [BB/G020434/1]
- SABR [BB/F005210/2]
- EPSRC [EP/I017267/1]
- BBSRC [BB/G020434/1] Funding Source: UKRI
- EPSRC [EP/I032223/1, EP/I017267/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/G020434/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/I017267/1, EP/I032223/1] Funding Source: researchfish
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Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy datasets. Over the years, a variety of heuristics have been proposed to solve this complex optimization problem, with good results in some cases yet with limitations in the biological setting. In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimization. Our method performs well even when the order of magnitude and/or the range of the parameters is unknown. The method refines iteratively a sequence of parameter distributions through local optimization combined with partial resampling from a historical prior defined over the support of all previous iterations. We exemplify our method with biological models using both simulated and real experimental data and estimate the parameters efficiently even in the absence of a priori knowledge about the parameters.
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