Global Quantitative Modeling of Chromatin Factor Interactions

Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions.Author Summary: Chromatin, like many other molecular biological systems, is composed of multiple interacting factors. Our knowledge about chromatin factors is mostly qualitative, and such qualitative knowledge can be insufficient for predicting collective behaviors. It's also extremely challenging to study collective behaviors involving multiple interacting factors through genetic and biochemical experiments. An alternative approach is to leverage large-scale genome-wide chromatin profiles and statistical modeling to create predictive models and infer underlying interaction mechanisms based on these observed high-throughput data. In this study, we developed a novel maximum entropy-based modeling approach to quantitatively capture interactions between chromatin factors at the same genomic location, which we see as a step toward quantitative understanding of chromatin organization that involves a system of multiple interacting factors. We applied this quantitative model to successfully infer functional properties of chromatin including interactions between chromatin factors. Furthermore, the model predicts unmeasured chromatin profiles with high accuracy based on its inferred dependencies with other factors within and across cell-types. Thus our modeling approach effectively ultilizes large-scale chromatin profiles to dissect chromatin factor interactions and to make data-driven inferences about chromatin regulation.