skip to main content

Title: Variance decomposition in stochastic simulators

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Authors:
 [1] ;  [2] ;  [3]
  1. LIMSI-CNRS, UPR 3251, Orsay (France)
  2. Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States)
  3. King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)
Publication Date:
OSTI Identifier:
22489544
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 142; Journal Issue: 24; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; 97 MATHEMATICAL METHODS AND COMPUTING; ALGORITHMS; DECOMPOSITION; INTERACTIONS; MATHEMATICAL SOLUTIONS; SENSITIVITY; SIMULATION; SIMULATORS; STOCHASTIC PROCESSES