title: An efficient and unbiased method for sensitivity analysis of stochastic reaction networks.
abstract: We consider the problem of estimating parameter sensitivity for Markovian models of reaction networks. Sensitivity values measure the responsiveness of an output with respect to the model parameters. They help in analysing the network, understanding its robustness properties and identifying the important reactions for a specific output. Sensitivity values are commonly estimated using methods that perform finite-difference computations along with Monte Carlo simulations of the reaction dynamics. These methods are computationally efficient and easy to implement, but they produce a biased estimate which can be unreliable for certain applications. Moreover, the size of the bias is generally unknown and hence the accuracy of these methods cannot be easily determined. There also exist unbiased schemes for sensitivity estimation but these schemes can be computationally infeasible, even for very simple networks. Our goal in this paper is to present a new method for sensitivity estimation, which combines the computational efficiency of finite-difference methods with the accuracy of unbiased schemes. Our method is easy to implement and it relies on an exact representation of parameter sensitivity that we recently proved elsewhere. Through examples, we demonstrate that the proposed method can outperform the existing methods, both biased and unbiased, in many situations.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland.
authors.forename: Ankit
authors.initials: A
authors.lastname: Gupta
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland mustafa.khammash@bsse.ethz.ch.
authors.forename: Mustafa
authors.initials: M
authors.lastname: Khammash
mesh_terms: D008962:Models, Theoretical; D009010:Monte Carlo Method; D013269:Stochastic Processes
keywords: Markov process; parameter sensitivity; stochastic reaction networks; unbiased estimator