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

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

SUBMITTER: Szedlak A 

PROVIDER: S-EPMC5711035 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature