RT Journal Article
SR Electronic
T1 Chromatin Structures from Integrated AI and Polymer Physics Model
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.11.27.624905
DO 10.1101/2024.11.27.624905
A1 Schultz, Eric R
A1 Kyhl, Soren
A1 Willett, Rebecca
A1 de Pablo, Juan J
YR 2024
UL http://biorxiv.org/content/early/2024/11/29/2024.11.27.624905.abstract
AB The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure from indirect measures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.Competing Interest StatementThe authors have declared no competing interest.