Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges.

Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.