Robust Moment Closure Method for the Chemical Master Equation
For researchers modeling stochastic biochemical reaction networks, this provides a theoretically grounded moment closure method with guaranteed worst-case optimality and error bounds.
The paper develops a moment closure technique for the Chemical Master Equation that approximates higher-order moments as affine functions of lower-order moments, proving optimality in a worst-case context and casting the problem as a tractable linear program. The method yields a finite-dimensional linear system with quantified approximation error.
The Chemical Master Equation (CME) is used to stochastically model biochemical reaction networks, under the Markovian assumption. The low-order statistical moments induced by the CME are often the key quantities that one is interested in. However, in most cases, the moments equation is not closed; in the sense that the first $n$ moments depend on the higher order moments, for any positive integer $n$. In this paper, we develop a moment closure technique in which the higher order moments are approximated by an affine function of the lower order moments. We refer to such functions as the affine Moment Closure Functions (MCF) and prove that they are optimal in the worst-case context, in which no a priori information on the probability distribution is available. Furthermore, we cast the problem of finding the optimal affine MCF as a linear program, which is tractable. We utilize the affine MCFs to derive a finite dimensional linear system that approximates the low-order moments. We quantify the approximation error in terms of the $% l_{\infty }$ induced norm of some linear system. Our results can be effectively used to approximate the low-order moments and characterize the noise properties of the biochemical network under study.