Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities. Issue 5 (1st October 2015)

• Record Type: Journal Article
• Authors: Zimmer, Christoph; Sahle, Sven
• Is Part Of: IET systems biology. Volume 9:Issue 5(2015)
• Page Start: 181
• Page End: 192
• Publication Date: 2015-10-01
• Subjects: stochastic systems -- parameter estimation -- probability -- least squares approximations; deterministic inference -- stochastic systems -- multiple shooting -- linear noise approximation -- transition probabilities -- systems biology -- parameter estimation methods -- likelihood function -- normal distributions -- intrinsic stochasticity effects -- reversible jump techniques -- approximative sampling -- conventional least‐squares parameter estimation methods; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2014.0020
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the 'multiple shooting for stochastic systems (MSS)' method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least‐squares parameter estimation methods.