Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

Journal Article · Fri Jul 10 00:00:00 EDT 2015 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0130825· OSTI ID:1456883

Arampatzis, Georgios ^[1]; Katsoulakis, Markos A. ^[1]; Pantazis, Yannis ^[1]; Lytton, William W. ^[1]

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1. Univ. of Massachusetts, Amherst, MA (United States). Dept. of Mathematics and Statistics

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here in this paper we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in “sloppy” systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.

• Research Organization: Univ. of Massachusetts, Amherst, MA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); European Union (EU)
• Grant/Contract Number: SC0010723
• OSTI ID: 1456883
• Journal Information: PLoS ONE, Vol. 10, Issue 7; ISSN 1932-6203
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

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97 MATHEMATICS AND COMPUTING