# Analytic solutions for stochastic hybrid models of gene regulatory networks

## TL;DR

This paper derives explicit analytic solutions for a class of hybrid stochastic models of gene regulatory networks, providing insights into their equilibrium behavior using advanced mathematical techniques.

## Contribution

It introduces a novel approach to solve hyperbolic PDE systems in hybrid gene expression models, explicitly characterizing their stationary solutions.

## Key findings

- Proved convergence to stationary solutions.
- Explicitly determined equilibrium distributions.
- Applied positive operator theory to biological models.

## Abstract

Discrete-state stochastic models are a popular approach to describe the inherent stochasticity of gene expression in single cells. The analysis of such models is hindered by the fact that the underlying discrete state space is extremely large. Therefore hybrid models, in which protein counts are replaced by average protein concentrations, have become a popular alternative. The evolution of the corresponding probability density functions is given by a coupled system of hyperbolic PDEs. This system has Markovian nature but its hyperbolic structure makes it difficult to apply standard functional analytical methods. We are able to prove convergence towards the stationary solution and determine such equilibrium explicitly by combining abstract methods from the theory of positive operators and elementary ideas from potential analysis.