Most disease associated GWAS variants have relatively modest effects on expression in reporter or CRISPR perturbation assays. In addition, enhancer disruption in vivo often has surprisingly weak phenotypic consequences. We hypothesize that a critical missing element is our lack of quantitative models of how multiple TFs interact at an enhancer, and how multiple enhancers interact at a locus to respond to perturbations in a nonlinear way through altered gene network activity. Predicting the impact of genomic variation thus requires quantitative modeling of how one variant's impact depends on other variants through their combined effect on altered cellular regulatory state. The central aim of this proposal is to develop computational methods to infer quantitative models of these combinatorial interactions by training on temporally-resolved measurements of gene activity, enhancer activity, and core cell fate-regulating transcription factor (TF) activity across cell state transitions in early human development. Our preliminary studies show that while promoter knockdown has robust effects on target gene expression, individual enhancer knockdown is often weaker and affects temporal transition dynamics, but not the final steady state. We show that gene network models based on sequence-based machine learning are consistent with these observations. We propose improvements to our sequence based models to develop kinetic rate equation and stochastic simulation gene network models to predict the variable and often temporal effects of enhancer perturbation. We will generate high time resolution ATAC, H3K27ac, and scRNA-seq data to train these models, and validate the gene network predictions of network response with CRISPRi in a native genomic context. We will first focus on our embryonic- stem-cell to definitive-endoderm (ESC-DE) system, and we will then develop methods to generalize application of these focused models to larger ENCODE regulatory datasets. Our work will enable a quantitative understanding of how the altered activity of regulatory elements affects the stability and dynamics of the gene regulatory networks within which the element operates, and how they play a role in controlling developmentally important and disease relevant cell state transitions.

We propose to develop improved sequence based machine learning models to build gene network models to more accurately predict the impact of enhancer perturbation by CRISPR or genetic variation. We will develop methods to train and validate these models by detecting altered TF activity from high time resolution chromatin accessibility, TF ChIP-seq, and H3K27ac data in a stem cell differentiation system and in large consortium datasets (e.g. ENCODE). This project will significantly contribute to our understanding of regulatory element function and how sequence variation impacts disease.