CAREER: Computational Modeling of the Structure, Dynamics, and Function of the Human Genome Under Mechanical Deformation

Our hereditary information is encoded within the ~3.2 billion base pairs of our genome’s double-stranded DNA. All this genomic material is contained within a cellular compartment called the nucleus, which has a diameter of ~1-10 micrometers. Scientists are increasingly finding that the nucleus is not just a passive container—forces acting on it can influence which parts of our DNA are turned on (i.e., which parts are being read). One idea is that when forces deform the nucleus, they change how chromosomes are arranged and move, which can influence which parts of the DNA are readable. To explore this idea, we will build realistic computer models of the human cell nucleus and its contents. We will study these models using simulations that act like a “computational microscope”, allowing us to see how physical forces affect the nucleus and what may go wrong in disease. Because these detailed structural changes cannot as easily be observed using experiment, this approach provides a powerful new window into how mechanical forces influence the organization of the genome. We will also zoom in with a “computational microscope” by studying the proteins that physically link chromosomes to the nucleus and connect the nucleus to its surroundings. These proteins facilitate the transmission of forces from outside of the nucleus to the genome. This project will involve undergraduate researchers in building structural models of these proteins using machine learning and artificial intelligence (AI). Given the increasing relevance of AI in everyday life, these efforts will train students to critically evaluate AI predictions and use them to refine physics-based models. This project advances national interests by addressing questions at the frontier of genomic biophysics while training the next generation of scientists at the interface of biology, chemistry, and physics in genome-scale physical modeling and AI interpretation. This project focuses on understanding the mechanistic details of how external forces applied to the human nucleus affects the structures and dynamics of the chromosomes contained within the nucleus. In other words, what are the structural consequences of nuclear deformation? There is growing evidence that the nucleus functions as a mechano-sensor capable of modulating gene expression in response to mechanical forces transmitted from the cellular environment. One hypothesis for this mechanical control of gene expression is that external forces deform the nucleus and affect the spatial organization of chromatin, particularly chromatin associated with the nuclear lamina, a filamentous network of proteins that line the inner nuclear envelope. Yet there currently exists no detailed structural understanding of how this mechanical signaling affects the structures and biological functions of the human genome. Building a theoretical understanding of these mechanistic details necessitates the creation of appropriate physical models of the human nucleus and its contents, which include details such as polymer connectivity and positions of the genes. Such a model could then be explored by computer simulations to provide a “computational microscope”, shedding light on this nuclear mechano-modulation but also providing structural insight as to what happens when this function is disrupted resulting in disease. This computational microscope will allow us to investigate the structural consequences of deformation at a level of detail that is not possible to achieve using experiment, and hence, offers a valuable window understanding how nuclear architecture is modulated by mechanical perturbations. To better understand this mechano-modulation at a finer scale, we also focus on the mechanics of the protein complexes that connect the cytoskeleton to chromatin. Specifically, we will use machine learning and artificial intelligence combined with traditional physics-based approaches to understand how protein complexes such as the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex mediate force propagation from the cytoskeleton to the chromatin within the nucleus. This project supports undergraduate student research that focuses on extracting structural models of these proteins and protein complexes using machine learning and artificial intelligence (AI), such as AlphaFold. These efforts will train undergraduate students to critically evaluate AI predictions and use them to refine physics-based models, which is important due to the growing relevance of AI. This project advances national interests by addressing questions at the frontier of genomic biophysics while training the next generation of scientists at the interface of biology, chemistry, and physics in genome-scale physical modeling and AI interpretation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.