Learning the dynamics and interaction of migrating cells | Chase Broedersz скачать в хорошем качестве

Learning the dynamics and interaction of migrating cells | Chase Broedersz

Recorded on 11/04/2025 Watch the recording without ads at https://www.nitmb.org/cytoskeletal-ma... Title: Learning the dynamics and interaction of migrating cells Speaker: Chase Broedersz Abstract: Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. Yet, the underlying dynamics of how cells move and interact with each other, and their environment is still unclear. We employ a data-driven approach to infer the dynamics of cell movement, morphology and interactions of cells confined in artificial environments. By inferring a stochastic equation of motion directly from experimental data, we show that cells exhibit intricate non-linear deterministic dynamics that adapt to the geometry of confinement. We extend this approach to interacting systems, by tracking the repeated collisions of confined pairs of cells. After identifying distinct interactions modes for diverse cell types, we show how these interactions can be captured by a unifying mechanistic model. Finally, I will discuss how our approach can be extended to the collective dynamics multicellular systems. This talk was recorded as part of the 'Machine Learning of Cytoskeletal Machines (Cell Migration and Mitosis)' workshop at NITMB Workshop Overview: Traditional bottom-up physical-mathematical models have longstanding popularity and success in studying cytoskeleton and mechanochemical machines driving cell movements and division. These models brought and will continue to bring mechanistic insights into cell migration. However, such models are either too simple to embrace the complexity of the multiscale cell processes or are hopelessly cumbersome and unwieldy to be used to nimbly test multiple hypotheses. Machine learning and AI approaches have demonstrated immense strength in identifying statistical patterns in cytoskeletal machines and in predicting cytoskeletal dynamics from microscopy data. However, these data-driven approaches largely neglect the laws of physics and chemistry needed to ground the discoveries in biological mechanisms. These complementary strengths and weaknesses between the traditional modeling and modern data-scientific approaches suggest a promising avenue forward: augmenting traditional models with data-scientific and AI methods for the sake of building more complex traditional models that can be directly connected with the enormous volumes of biological data of cytoskeletal machines. This workshop will convene data scientists, experimental biologists, mathematical modelers and biophysicists using or interested in starting to use ML to study cytoskeletal dynamics, cell migration and mitosis. The goal is to foster an exchange of ideas between these research communities. The workshop is structured to help participants identify the most promising opportunities for developing and using ML tools to answer biological questions. The program includes both overview and research talks, poster sessions and lightning talks by poster presenters, and will have ample time for participants to get to know each other, exchange ideas and foster collaborations. 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.