Classification-Based Inference of Dynamical Models of Gene Regulatory Networks.

ABSTRACT: Cell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimizati

SUBMITTER: Fehr DA 

PROVIDER: S-EPMC6893186 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature