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Title: Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

Abstract

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 inmore » 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.« less

Authors:
 [1];  [1];  [1];  [1]
  1. Univ. of Massachusetts, Amherst, MA (United States). Dept. of Mathematics and Statistics
Publication Date:
Research Org.:
Univ. of Massachusetts, Amherst, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); European Union (EU)
OSTI Identifier:
1456883
Grant/Contract Number:  
SC0010723
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 10; Journal Issue: 7; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Arampatzis, Georgios, Katsoulakis, Markos A., Pantazis, Yannis, and Lytton, William W. Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks. United States: N. p., 2015. Web. doi:10.1371/journal.pone.0130825.
Arampatzis, Georgios, Katsoulakis, Markos A., Pantazis, Yannis, & Lytton, William W. Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks. United States. doi:10.1371/journal.pone.0130825.
Arampatzis, Georgios, Katsoulakis, Markos A., Pantazis, Yannis, and Lytton, William W. Fri . "Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks". United States. doi:10.1371/journal.pone.0130825. https://www.osti.gov/servlets/purl/1456883.
@article{osti_1456883,
title = {Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks},
author = {Arampatzis, Georgios and Katsoulakis, Markos A. and Pantazis, Yannis and Lytton, William W.},
abstractNote = {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.},
doi = {10.1371/journal.pone.0130825},
journal = {PLoS ONE},
number = 7,
volume = 10,
place = {United States},
year = {2015},
month = {7}
}

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