Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

TL;DR

This paper models cell cycle gene expression dynamics using cyclic Hopfield systems, demonstrating the ability to simulate experimental data, analyze noise effects, and identify gene inhibition strategies to disrupt cell cycle progression.

Contribution

It introduces a novel application of cyclic Hopfield models to encode and analyze cell cycle gene expression time series data, including gene inhibition strategies.

Findings

Simulated cell populations can recreate experimental gene expression data.

Inhibition of specific kinases causes cell cycle arrest in simulations.

Noise impacts the stability and dynamics of the modeled cell cycle.

Abstract

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.