Gene regulatory network structure informs the distribution of perturbation effects

Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.Author summary: Gene regulatory networks (GRNs) describe the causal relationships by which gene expression is controlled in the cell. How these networks are structured and how this organization relates to their function is a central problem in biology. Here, we propose a framework for simulating GRNs that consists of two parts: an algorithm to create graph structures and a mathematical model of gene expression. We characterize the effects of the parameters of our model, showing how they affect properties of networks and experimental data. Specifically, we find that key structural properties of biological networks–sparsity, modular groups, and degree dispersion–are consistent with patterns in real data and tend to dampen the effects of gene perturbations. We showcase the utility of our model with vignettes from a synthetic network that has properties similar to those of an experimentally assayed GRN. We then describe challenges and opportunities for inference, using graph properties as a lens to consider ways to map the causal and functional relationships between genes in future work.