Advances in high-speed computational platforms and innovative algorithms are opening opening opportunities  for modeling in biology as never before [1, 2]. In turn, these advances are driving biology and medicine forward, as evidenced during the Covid-19 pandemic [3]. 

While there are many general mathematical methods that can be applied widely like linear algebra routines and fast summation algorithms, the most successful approaches are tailored and tightly connected with both the application at hand and the computing platform. Genome organization is a prominent area where a variety of models and methods — from atomistic to polymer levels — is critically needed to bridge experimental data and push science frontiers. Genome organization refers to the folding of the genome material, or the chromatin fiber that makes up chromosomes, in the cell nucleus of higher organisms. 

This folding and dynamics of the chromatin fiber regulates life’s essential processes like gene expression, DNA repair, and cell differentiation, and thusimpacts human disease. As our appreciation for the diversity and flexibility of DNA on the base-pair level has deepened (e.g., [4]), its large-scale bending and coiling around histone proteins to form the chromosomal material in higher organisms has posed many structural and mechanistic questions [5–8]. The genomic information in the DNA is packaged in a hierarchy of levels, from the nucleosome tocondensed chromatin fibers to chromosomes and chromosomal territories. Profound questions regarding DNA geometry, topology, and function span from the single nucleosome/base-pair level to condensed chromosomal arrangements on the mega base-pair level in the metaphasecell cycle. We lack an understanding of both structures and kinetics of the chromatin fiber and chromosomosal arrangements. These transitions are tightly controlled by a host of proteins, which can directly bind to the chromatin fiber or induce chemical modifications of DNA and histones, as well as long noncoding RNAs. Cohesin and condensin complexes also operate to regulate gene expression as molecular machines in an energy-dependent manner [9–13].

Theoretical and computational physics have played a pivotal role in advancing our understanding of these processes, offering models that describe the dynamic behavior of chromatin loops and that predict how physical parameters influence resulting genome organization [14]. Despite major progress, this field has major open questions. At the molecular level, mechanochemical mechanisms along DNA remain unclear, as do the influences of DNA longitudinal and torsional tension, nucleosome positioning, and transcriptional activity on loop extrusion dynamics [15]. Interdisciplinary research combining theoretical modeling, single-molecule biophysics, and high-resolution imaging continues to advance our understanding of these active processes and their implications for cellular function and disease [16]. In addition to a bridging between modeling and experimentation on nucleosome and fiber levels with genome studies on the kilo-base level  [5, 17], tailored multiscale computational approaches are needed to help interpret experimental structural and kinetic data.

Our program targeted for early 2026 continues a very successful dialogue among the mathematics, physics, biology, chemistry, and scientific computing communities started in Les Houches in 2017 and continued at ESI in March 2024. Our proposed program for 2026 aims to continue to bring these scientists together to discuss the current state-of-the-art in chromatin modeling, identify future challenges, inspire new collaborations and multiscale integrative approaches, and help educate a new generation of multidisciplinary scientists. The increasing amount of genomic chromosome capture data at varying levels of resolution for both single cells and multiple cell populations, as well as the increasing potential of AI approaches will be important areas of discuss and explore.

We will assemble  a leading group of collaborative and broad mathematical biologists and physicists, computational biophysicists, and experimentalists to address these multiscale challenges and establish/ continue collaborations and scientific idea exchange. We would like to continue to inspire more scientists to work in this fascinating area of biophysics/ molecular biology and encourage more of these cross-discipline and multiscale experimental/ theory/ modeling collaborations in our workshop.