Master regulators as order parameters of gene expression states

TL;DR

This paper models cell type-specific gene expression as memory states in a Hopfield network, linking order parameters to master regulators and defining an epigenetic landscape with stable cell states, applied to hematopoiesis data.

Contribution

It introduces a novel interpretation of Hopfield network order parameters as master regulators, connecting neural network theory with gene regulation and cell state stability.

Findings

Order parameters correspond to master regulator concentrations.

The model defines an epigenetic landscape with stable cell states.

Application to hematopoiesis data demonstrates the model's relevance.

Abstract

Cell type-specific gene expression patterns are represented as memory states of a Hopfield neural network model. It is shown that order parameters of this model can be interpreted as concentrations of master transcription regulators that form concurrent positive feedback loops with a large number of downstream regulated genes. The order parameter free energy then defines an epigenetic landscape in which local minima correspond to stable cell states. The model is applied to gene expression data in the context of hematopoiesis.