NIH R01 · 2024
Sequence-based Machine Learning for Inference of Dynamic Cell State Gene Network Models
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…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.