Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of the chromosomes.

The organized three-dimensional chromosome architecture in the cell nucleus provides the scaffolding for the precise regulation of gene expression. When the cell changes its identity in the cell fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, the rapid development of experimental methods has provided unprecedented data on characterizing the hierarchical structures and the dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ("top-down") and physics-based ("bottom-up") categories. We discuss their contributions in offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes. We highlight the perspectives on future efforts in developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.