Analytic solution of chemical master equations involving gene switching. I: Representation theory and diagrammatic approach to exact solution

TL;DR

This paper introduces a novel analytic method using quantum-inspired tools to solve complex chemical master equations involving gene switching, enabling exact solutions and deeper understanding of stochastic gene expression models.

Contribution

The paper presents a new analytic approach leveraging quantum mechanics tools to solve CME models with gene switching, providing exact solutions and insights into stochastic transcription dynamics.

Findings

Exact solutions for bursty transcription models derived

Analytic expressions for gene switching dynamics obtained

Framework applicable to complex splicing and gene regulation models

Abstract

The chemical master equation (CME), which describes the discrete and stochastic molecule number dynamics associated with biological processes like transcription, is difficult to solve analytically. It is particularly hard to solve for models involving bursting/gene switching, a biological feature that tends to produce heavy-tailed single cell RNA counts distributions. In this paper, we present a novel method for computing exact and analytic solutions to the CME in such cases, and use these results to explore approximate solutions valid in different parameter regimes, and to compute observables of interest. Our method leverages tools inspired by quantum mechanics, including ladder operators and Feynman-like diagrams, and establishes close formal parallels between the dynamics of bursty transcription, and the dynamics of bosons interacting with a single fermion. We focus on two problems: (i) the chemical birth-death process coupled to a switching gene/the telegraph model, and (ii) a model of transcription and multistep splicing involving a switching gene and an arbitrary number of downstream splicing steps. We work out many special cases, and exhaustively explore the special functionology associated with these problems. This is Part I in a two-part series of papers; in Part II, we explore an alternative solution approach that is more useful for numerically solving these problems, and apply it to parameter inference on simulated RNA counts data.