Mechanical Information Processing in Adherent Cells | Margaret Gardel

Recorded on 09/15/2026 Watch the recording without ads at https://www.nitmb.org/event/biologica... Speaker: Margaret Gardel Title: Mechanical Information Processing in Adherent Cells Part of the Biological Function in Space and Time: From Forces and Cues to Emerging Decision-making in Fates, States, and Shapes workshop Workshop Overview: Biological systems are physical systems operating in space and time. Understanding how they leverage dynamics and spatial extension to achieve functions, transforming physical cues into decisions, is a compelling challenge which requires new mathematical frameworks to identify and characterize emerging patterns in modern high-dimensional data, to quantify reproducibility, to describe how the information is encoded and transferred across scales, and to elucidate decision-making mechanisms. The goal of this workshop is to bring together experimental biologists, specialists in data analysis and theorists to identify current challenges, establish collaborations, and discuss strategies to advance our understanding of how biological systems maintain and organize function across space and time. Researchers across biological subfields have developed formalisms and concepts adapted to their systems of interest. By breaking the traditional barriers of scale, we hope this meeting can be a way of identifying universal hallmarks disseminated across fields. NITMB Overview: The NSF-Simons National Institute for Theory and Mathematics in Biology (NITMB) aims to integrate the disciplines of mathematics and biology in order to transform the practice of biological research and to inspire new mathematical discoveries. NITMB is a partnership between Northwestern University and the University of Chicago. It is funded by the National Science Foundation DMS-2235451 and the Simons Foundations MP-TMPS-00005320. The mission of the NITMB is to create a nationwide collaborative research community that will generate new mathematical results and uncover the “rules of life” through theories, data-informed mathematical models, and computational and statistical tools.