Inferring gene regulatory networks using transcriptional profiles as dynamical attractors

Abstract

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN. Author SummaryThe establishment of distinct transcriptional programs, where specific sets of genes are activated or repressed, is fundamental to all forms of life. Sequence-specific DNA-binding proteins, often referred to as regulatory transcription factors, form interconnected gene regulatory networks (GRNs) which underlie the establishment and maintenance of specific transcriptional programs. Since their discovery, many modeling approaches have sought to understand the structure and regulatory behaviors of these GRNs. The field of GRN inference uses experimental measurements of transcript abundance to predict how regulatory transcription factors interact with their downstream target genes to establish specific transcriptional programs. However, most prior approaches have been limited by the exclusive use of "static" or steady-state measurements. We have developed a unique approach which incorporates dynamic transcriptional data into a sophisticated ordinary differential equation model to infer GRN structures that give rise to distinct transcriptional programs. Our model not only outperforms six other leading models, it also is capable of accurately predicting how changes in GRN structure will impact the resulting transcriptional programs. These unique attributes of our model, combined with "real world" experimental validation of our model predictions, represent a significant advance in the field of gene regulatory network inference.