Algebraic structure of hierarchic first-order reaction networks applicable to models of clone size distribution and stochastic gene expression

TL;DR

This paper introduces a Lie theory-based method to analyze hierarchic first-order reaction networks, revealing their algebraic structure and applying it to models of population dynamics and gene transcription.

Contribution

It presents a novel Lie group decomposition approach for hierarchic reaction networks, enabling more systematic analysis of complex biological stochastic processes.

Findings

Lie group associated with hierarchic networks decomposes as a wreath product

Method applied to population dynamics and gene transcription models

Provides solutions critical for parameter inference in biological models

Abstract

In biology, stochastic branching processes with a two-stage, hierarchical structure arise in the study of population dynamics, gene expression, and phylogenetic inference. These models have been commonly analyzed using generating functions, the method of characteristics and various perturbative approximations. Here we describe a general method for analyzing hierarchic first-order reaction networks using Lie theory. Crucially, we identify the fact that the Lie group associated to hierarchic reaction networks decomposes as a wreath product of the groups associated to the subnetworks of the independent and dependent types. After explaining the general method, we illustrate it on a model of population dynamics and the so-called two-state or telegraph model of single-gene transcription. Solutions to such processes provide essential input to downstream methods designed to attempt to infer parameters of these and related models.