ArticleIET systems biology2015
Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities.
Article in IET systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data.Frontiers in computational neuroscience · 2025Article
- Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level.PLoS computational biology · 2022Article
- Tracking and predicting U.S. influenza activity with a real-time surveillance network.PLoS computational biology · 2020Article
- Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models.Statistical methods in medical research · 2019Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
- Use of daily Internet search query data improves real-time projections of influenza epidemics.Journal of the Royal Society, Interface · 2018Article
- A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study.BMC systems biology · 2017Article
- A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models.PLoS computational biology · 2017Article
- Generalized method of moments for estimating parameters of stochastic reaction networks.BMC systems biology · 2016Article
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2 authors.
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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.
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