Unbiased estimation of second-order parameter sensitivities for stochastic reaction networks

TL;DR

This paper introduces a new unbiased estimator for second-order parameter sensitivities in stochastic reaction networks, improving accuracy and efficiency in biological modeling and parameter optimization.

Contribution

The paper develops the Double Bernoulli Path Algorithm, a novel method for unbiased second-order sensitivity estimation with lower variance than existing approaches.

Findings

The new estimator is unbiased and more efficient than Girsanov-based methods.

Numerical examples demonstrate the estimator's improved performance.

The method facilitates better parameter inference and control design in stochastic systems.

Abstract

Stochastic models for chemical reaction networks are increasingly popular in systems and synthetic biology. These models formulate the reaction dynamics as Continuous-Time Markov Chains (CTMCs) whose propensities are parameterized by a vector $\theta$ and parameter sensitivities are introduced as derivatives of their expected outputs with respect to components of the parameter vector. Sensitivities characterise key properties of the output like robustness and are also at the heart of numerically efficient optimisation routines like Newton-type algorithms used in parameter inference and the design of of control mechanisms. Currently the only unbiased estimator for second-order sensitivities is based on the Girsanov transform and it often suffers from high estimator variance. We develop a novel estimator for second-order sensitivities by first rigorously deriving an integral representation of these sensitivities. We call the resulting method the Double Bernoulli Path Algorithm and illustrate its efficiency through numerical examples.