Analysis of tensor methods for stochastic models of gene regulatory networks

Abstract

The tensor-structured parametric analysis (TPA) has been recently developed for simulating and analysing stochastic behaviours of gene regulatory networks [Liao et. al., 2015]. The method employs the Fokker-Planck approximation of the chemical master equation, and uses the Quantized Tensor Train (QTT) format, as a low-parametric tensor-structured representation of classical matrices and vectors, to approximate the high-dimensional stationary probability distribution. This paper presents a detailed error analysis of all approximation steps of the TPA regarding validity and accuracy, including modelling error, artificial boundary error, discretization error, tensor rounding error, and algebraic error. The error analysis is illustrated using computational examples, including the death-birth process and a 50-dimensional isomerization reaction chain.