Approximating stochastic biochemical processes with Wasserstein pseudometrics
Approximating stochastic biochemical processes with Wasserstein pseudometrics
- Author(s): D. Thorsley and E. Klavins
- DOI: 10.1049/iet-syb.2009.0039
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- Author(s): D. Thorsley 1 and E. Klavins 1
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View affiliations
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Affiliations:
1: Department of Electrical Engineering, University of Washington, Seattle, USA
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Affiliations:
1: Department of Electrical Engineering, University of Washington, Seattle, USA
- Source:
Volume 4, Issue 3,
May 2010,
p.
193 – 211
DOI: 10.1049/iet-syb.2009.0039 , Print ISSN 1751-8849, Online ISSN 1751-8857
Modelling stochastic processes inside the cell is difficult due to the size and complexity of the processes being investigated. As a result, new approaches are needed to address the problems of model reduction, parameter estimation, model comparison and model invalidation. Here, the authors propose addressing these problems by using Wasserstein pseudometrics to quantify the differences between processes. The method the authors propose is applicable to any bounded continuous-time stochastic process and pseudometrics between processes are defined only in terms of the available outputs. Algorithms for approximating Wasserstein pseudometrics are developed from experimental or simulation data and demonstrate how to optimise parameter values to minimise the pseudometrics. The approach is illustrated with studies of a stochastic toggle switch and of stochastic gene expression in E. coli.
Inspec keywords: cellular biophysics; genetics; stochastic processes; biology computing; biochemistry
Other keywords:
Subjects: Physics of subcellular structures; Biology and medical computing; Probability theory, stochastic processes, and statistics; Physical chemistry of biomolecular solutions and condensed states; Probability and statistics
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