Ongoing efforts to connect molecular and cellular scale behaviors in microtubule self-organization

Recorded on 11/03/2025
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Title: Ongoing efforts to connect molecular and cellular scale behaviors in microtubule self-organization
Speaker: Daniel Needleman
Abstract: How microtubules self-organize to form different structures in different contexts, such as the spindle during cell division and bundles in neuronal processes, remains poorly understood. My group is studying this issue by integrating different experimental modalities that provide quantitative information at different time-scales and length-scales, and interpreting our results with biophysical theories. A key difficulty in
developing multiscale theories of microtubule self-organization is our lack of
understanding of the relevant physics and behaviors at the scale of tens of nanometers, which is the bridge between the molecular and cellular scales. Measurements at such mesoscales have been challenging. I will describe our ongoing efforts to leverage recent advances in cryo-electron microscopy to study mesoscale behaviors of the microtubule cytoskeleton. Our work so far has focused on developing new cryo-electron microscopy data analysis procedures, including using approaches pioneered by the machine learning community.

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.