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

Journal Article · Thu Sep 06 00:00:00 EDT 2018 · Journal of Statistical Software

DOI:https://doi.org/10.18637/jss.v085.i03· OSTI ID:1509830

Hu, Weilong ^[1]; Pantazis, Yannis ^[2]; Katsoulakis, Markos A. ^[2]

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1. Univ. of Massachusetts, Amherst, MA (United States); Univ. of Massachusetts, Amherst, MA (United States)
2. Univ. of Massachusetts, Amherst, MA (United States)

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.

• Research Organization: Univ. of Massachusetts, Amherst, MA (United States)
• Sponsoring Organization: Defense Advanced Research Projects Agency (DARPA) (United States); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• Grant/Contract Number: SC0010723
• OSTI ID: 1509830
• Journal Information: Journal of Statistical Software, Journal Name: Journal of Statistical Software Journal Issue: 3 Vol. 85; ISSN 1548-7660
• Publisher: Foundation for Open Access Statistics
• Country of Publication: United States
• Language: English

Related Subjects

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