Classification-Based Inference of Dynamical Models of Gene Regulatory Networks

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.

Development is controlled by gene regulatory networks (GRNs) that integrate extrinsic signals and intrinsic cell state to make decisions about cell fate

In summary, we have exploited features of the mathematical structure of gene circuits to break a difficult optimization problem into a series of two, much simpler, optimization problems. We have demonstrated that FIGR is effective on synthetic as well as experimental data from a biologically realistic GRN. We have validated the inferred gap gene model by comparing its parameters against models inferred with SA as well as comparing its output against experimental data. The improvement in computational efficiency and scalability should allow the inference of much larger GRNs than was possible previously.