Speaker
Description
One of the central goals of computational biology is to build a realistic model of a cell – one that can simulate cellular processes, predict the effects of perturbations, and support rational drug design. This vision has gained new momentum through recent technological advances, for example, in single-cell omics, structural proteomics, and visual mapping of cells using in-cell cryo-electron tomography (cryo-ET) and volume electron microscopy. The emergence of AI-based methods such as AlphaFold and other AI-driven models of biological complexity further contributes to this renewed interest. Although the form of such a comprehensive cell model remains undefined, the recent technological advances now allow us to build components that were previously out of reach. In this context, I will present our contribution toward modeling a cell by leveraging some of these recent technological advances. I will show the computational tools we are developing as part of an effort to build a computational framework for in-cell structural systems biology, aimed at enabling spatial and systems-level mapping of the cellular environment. These will include methods for data-driven modeling macromolecular assemblies and interaction networks, as well as approaches for identifying and modeling molecules and membranes in cryo-ET data.