Stochastic gene expression as a many body problem

TL;DR

This paper maps stochastic gene expression dynamics onto quantum many-body problems, revealing how gene networks' stability and attractor landscapes depend on network frustration, with implications for understanding cell types.

Contribution

It introduces a novel approach to model gene expression as quantum many-body systems, linking frustration in magnetic systems to gene network stability.

Findings

Number of attractors depends on network frustration.

Weakly frustrated networks have fewer stable attractors.

Gene network behavior parallels quantum spin systems.

Abstract

Gene expression has a stochastic component owing to the single molecule nature of the gene and the small number of copies of individual DNA binding proteins in the cell. We show how the statistics of such systems can be mapped on to quantum many body problems. The dynamics of a single gene switch resembles the spin boson model of a two site polaron or an electron transfer reaction. Networks of switches can be approximately described as quantum spin systems by using an appropriate variational principle. In this way the concept of frustration for magnetic systems can be taken over into gene networks. The landscape of stable attractors depends on the degree and style of frustration, much as for neural networks. We show the number of attractors, which may represent cell types, is much smaller for appropriately designed, weakly frustrated stochastic networks than for randomly connected networks.