Current Advances in Genome Modeling Across Length Scales

Abstract

The physical organization of DNA within the nucleus is fundamental to a wide range of biological processes. The experimental investigation of the structure of genomic DNA remains challenging due to its large size and hierarchical arrangement. These challenges present considerable opportunities for combined experimental and modeling approaches. Physics-based computational models, in particular, have emerged as essential tools for probing chromatin structure and dynamics across a wide range of length scales. Such models must necessarily be capable of bridging scales, and each scale presents its own subtleties and intricacies. This review discusses recent methodological advances in genomic structural modeling, emphasizing the need for multiscale integration to capture the hierarchical organization and molecular mechanisms that underlie chromatin structure and function. We present an analysis of state-of-the-art methods, as well as a perspective on challenges and future opportunities across length scales ranging from bare DNA to nucleosomes and chromatin fibers, up to TAD and chromosome-scale models. We emphasize models that connect genome organization to gene expression, models that leverage emerging machine learning capabilities, and models that develop multiscale approaches. We examine gaps in experimental data that computational models are poised to address and propose directions for future research that bridge theory and experiment in DNA structural biology.