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Title: Hybrid pathwise sensitivity methods for discrete stochastic models of chemical reaction systems

Stochastic models are often used to help understand the behavior of intracellular biochemical processes. The most common such models are continuous time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of expectations of model output quantities with respect to model parameters, are useful in this setting for a variety of applications. In this paper, we introduce a class of hybrid pathwise differentiation methods for the numerical estimation of parametric sensitivities. The new hybrid methods combine elements from the three main classes of procedures for sensitivity estimation and have a number of desirable qualities. First, the new methods are unbiased for a broad class of problems. Second, the methods are applicable to nearly any physically relevant biochemical CTMC model. Third, and as we demonstrate on several numerical examples, the new methods are quite efficient, particularly if one wishes to estimate the full gradient of parametric sensitivities. The methods are rather intuitive and utilize the multilevel Monte Carlo philosophy of splitting an expectation into separate parts and handling each in an efficient manner.
Authors:
 [1] ;  [2]
  1. Department of Mathematics and Computer Science, Saint Mary’s College, Notre Dame, Indiana 46556 (United States)
  2. Department of Mathematics, University of Wisconsin—Madison, Madison, Wisconsin 53706 (United States)
Publication Date:
OSTI Identifier:
22415994
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 142; Journal Issue: 3; 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; BIOCHEMISTRY; CHAINS; CHEMICAL REACTIONS; HYBRIDIZATION; MARKOV PROCESS; MONTE CARLO METHOD; SENSITIVITY