ArticleBioinformatics (Oxford, England)2016
Piecewise parameter estimation for stochastic models in COPASI.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data.Frontiers in computational neuroscience · 2025Article
- Bayesian inference of distributed time delay in transcriptional and translational regulation.Bioinformatics (Oxford, England) · 2020Article
- Generalized method of moments for estimating parameters of stochastic reaction networks.BMC systems biology · 2016Article
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3 authors.
Funding
Abstract
motivationComputational modeling is widely used for deepening the understanding of biological processes. Parameterizing models to experimental data needs computationally efficient techniques for parameter estimation. Challenges for parameter estimation include in general the high dimensionality of the parameter space with local minima and in specific for stochastic modeling the intrinsic stochasticity.
resultsWe implemented the recently suggested multiple shooting for stochastic systems (MSS) objective function for parameter estimation in stochastic models into COPASI. This MSS objective function can be used for parameter estimation in stochastic models but also shows beneficial properties when used for ordinary differential equation models. The method can be applied with all of COPASI's optimization algorithms, and can be used for SBML models as well. AVAILABILITY AND IMPLEMENTATION: The methodology is available in COPASI as of version 4.15.95 and can be downloaded from http://www.copasi.org CONTACT: frank.bergmann@bioquant.uni-heidelberg.de or fbergman@caltech.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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