Physics of the living genome
The long genome — roughly one meter in a human cell — undergoes severe compaction to fit inside a nucleus only ten microns across. Rather than collapsing into a featureless globule, the genome self-organizes into a rich hierarchical structure: chromosomes occupy separate territories, compartments of open and closed chromatin are interspersed, and topological domains partition the chromosome at the megabase scale.
Our approach is grounded in the polymeric nature of the genome. We microscopically model living DNA as a twistable semiflexible polymer where protein-mediated kinks and bends act as sinks or sources of bending and twisting energies at hundreds of base pairs. Our coarse-grained view, considering ~50 kb units, is that of a partially collapsed polymer where active mechanisms drive 3D contacts between far-away genomic units. We have shown that effective representations can capture many aspects of genome organization. Our goal is to learn the physics of these active mechanisms and decipher the regulatory code of the living genome.
Energy landscape modeling
We construct energy landscape models grounded in statistical mechanics to understand how proteins — particularly cohesin-mediated loop extrusion — along with phase separation and lamina adhesion effectively drive genome organization. These frameworks integrate molecular dynamics simulations with experimental Hi-C and imaging data to connect structure to function.
- Brahmachari S, Oliveira AB Jr, Mello MF, et al. Exploring the Energy Landscape of Bacterial Chromosome Segregation. Proc Natl Acad Sci USA. 2026.
- Brahmachari S, Contessoto VG, Di Pierro M, et al. Shaping the Genome via Lengthwise Compaction, Phase Separation, and Lamina Adhesion. Nucleic Acids Res. 2022.
- Oliveira AB Jr, Mello MF, Oliveira RJ, et al. A Data-Driven Chromatin Model Reveals Spatial and Dynamic Features of Genome Organization. Proc Natl Acad Sci USA. 2026.
- Contessoto VG, Oliveira AB Jr, Brahmachari S, et al. Energy Landscape Analysis of the Development of the Chromosome Structure Across the Cell Cycle. Proc Natl Acad Sci USA. 2025.
- Hoencamp C, Elbatsh AMO, Dudchenko O, Brahmachari S, et al. 3D Genomics Across the Tree of Life Reveals Condensin II as a Determinant of Architecture Type. Science. 2021.
- Ruben BS, Brahmachari S, Contessoto VG, et al. Structural Reorganization and Relaxation Dynamics of Axially Stressed Chromosomes. Biophys J. 2023.
Active mechanisms
Living cells are inherently out of equilibrium — motor proteins consume energy to actively remodel chromatin, RNA polymerases twist DNA as they transcribe, and correlated active forces drive chromosome dynamics. We develop theoretical and computational frameworks to study active forces, motor-driven loop extrusion, torsional stress propagation from transcription, and how non-equilibrium fluctuations shape genome organization and gene regulation.
- Brahmachari S, Tripathi S, Onuchic JN, et al. Nucleosomes Play a Dual Role in Regulating Transcription Dynamics. Proc Natl Acad Sci USA. 2024.
- Tripathi S, Brahmachari S, Onuchic JN, et al. DNA Supercoiling-Mediated Collective Behavior of Co-Transcribing RNA Polymerases. Nucleic Acids Res. 2022.
- Hwang J, Lee CY, Brahmachari S, et al. DNA Supercoiling-Mediated G4/R-loop Formation Tunes Transcription by Controlling the Access of RNA Polymerase. Nat Commun. 2025.
- Brahmachari S, Markovich T, MacKintosh FC, et al. Temporally Correlated Active Forces Drive Segregation and Enhanced Dynamics in Chromosome Polymers. PRX Life. 2024.
- Brahmachari S, Marko JF. Chromosome Disentanglement Driven Via Optimal Compaction of Loop-Extruded Brush Structures. Proc Natl Acad Sci USA. 2019.
Physics-based AI
Pure data-driven AI models lack physical interpretability; pure physics models struggle to capture the complexity of biological systems. We are building AI models that are grounded in physical principles — incorporating known symmetries, conservation laws, and mechanistic constraints — to predict genome structure, infer model parameters from experimental data, and generate physically meaningful insights.