ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks

doi:10.18637/jss.v085.i03

Title: ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks

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Abstract

Stochastic simulation and modeling play an important role to elucidate the fundamental mechanisms in complex biochemical networks. The parametric sensitivity analysis of reaction networks becomes a powerful mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, due to overwhelming computational cost, parametric sensitivity analysis is a extremely challenging problem for stochastic models with a high-dimensional parameter space and for which existing approaches are very slow. Here we present an information-theoretic sensitivity analysis in path-space (ISAP) MATLAB package that simulates stochastic processes with various algorithms and most importantly implements a gradient-free approach to quantify the parameter sensitivities of stochastic chemical reaction network dynamics using the pathwise Fisher information matrix (PFIM; Pantazis, Katsoulakis, and Vlachos 2013). The sparse, block-diagonal structure of the PFIM makes its computational complexity scale linearly with the number of model parameters. As a result of the gradientfree and the sparse nature of the PFIM, it is highly suitable for the sensitivity analysis of stochastic reaction networks with a very large number of model parameters, which are typical in the modeling and simulation of complex biochemical phenomena. Finally, the PFIM provides a fast sensitivity screening method (Arampatzis, Katsoulakis, and Pantazis 2015) whichmore »« less

Authors:

Hu, Weilong ^[1]; Pantazis, Yannis ^[1]; Katsoulakis, Markos A. ^[1]

Univ. of Massachusetts, Amherst, MA (United States)

Publication Date:: 2018-09-06

Research Org.:: Univ. of Massachusetts, Amherst, MA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); Defense Advanced Research Projects Agency (DARPA) (United States)

OSTI Identifier:: 1509830

Grant/Contract Number:: SC0010723; W911NF1520122

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Statistical Software

Additional Journal Information:: Journal Volume: 85; Journal Issue: 3; Journal ID: ISSN 1548-7660

Publisher:: Foundation for Open Access Statistics

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; stochastic biochemical networks; parametric sensitivity analysis; high-dimensional parameter space; pathwise Fisher information matrix; fast sensitivity screening

Citation Formats


                    Hu, Weilong, Pantazis, Yannis, and Katsoulakis, Markos A. ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks.  United States: N. p., 2018. 
        Web.  doi:10.18637/jss.v085.i03.

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                    Hu, Weilong, Pantazis, Yannis, & Katsoulakis, Markos A. ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks.  United States.  doi:10.18637/jss.v085.i03.

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                    Hu, Weilong, Pantazis, Yannis, and Katsoulakis, Markos A. Thu .  
        "ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks".  United States.  doi:10.18637/jss.v085.i03.  https://www.osti.gov/servlets/purl/1509830.

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@article{osti_1509830,

  title        = {ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks},

  author       = {Hu, Weilong and Pantazis, Yannis and Katsoulakis, Markos A.},

  abstractNote = {Stochastic simulation and modeling play an important role to elucidate the fundamental mechanisms in complex biochemical networks. The parametric sensitivity analysis of reaction networks becomes a powerful mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, due to overwhelming computational cost, parametric sensitivity analysis is a extremely challenging problem for stochastic models with a high-dimensional parameter space and for which existing approaches are very slow. Here we present an information-theoretic sensitivity analysis in path-space (ISAP) MATLAB package that simulates stochastic processes with various algorithms and most importantly implements a gradient-free approach to quantify the parameter sensitivities of stochastic chemical reaction network dynamics using the pathwise Fisher information matrix (PFIM; Pantazis, Katsoulakis, and Vlachos 2013). The sparse, block-diagonal structure of the PFIM makes its computational complexity scale linearly with the number of model parameters. As a result of the gradientfree and the sparse nature of the PFIM, it is highly suitable for the sensitivity analysis of stochastic reaction networks with a very large number of model parameters, which are typical in the modeling and simulation of complex biochemical phenomena. Finally, the PFIM provides a fast sensitivity screening method (Arampatzis, Katsoulakis, and Pantazis 2015) which allows it to be combined with any existing sensitivity analysis software.},

  doi          = {10.18637/jss.v085.i03},

  journal      = {Journal of Statistical Software},

  number       = 3,

  volume       = 85,

  place        = {United States},

  year         = {2018},

  month        = {9}

}

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