Dataset Information
Piecewise parameter estimation for stochastic models in COPASI.
Ontology highlight
ABSTRACT: Motivation
Computational 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.Results
We 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.orgContact
frank.bergmann@bioquant.uni-heidelberg.de or fbergman@caltech.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Bergmann FT
PROVIDER: S-EPMC6169462 | biostudies-literature | 2016 May
REPOSITORIES: biostudies-literature
Publications
Piecewise parameter estimation for stochastic models in COPASI.
Bioinformatics (Oxford, England) 20160118 10
<h4>Motivation</h4>Computational 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.<h4>Results</h4>We implemented the recently suggested multiple shooting for stochastic ...[more]