Decoding behavioral algorithms in single cells | Ben Larson

Recorded on 11/05/2025
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Title: Decoding behavioral algorithms in single cells
Speaker: Ben Larson
Abstract: Single cells can display remarkably sophisticated behaviors, managing the flow of information and orchestrating intricate processes to carry out essential functions. Proper execution of cellular behavior is essential not only for survival of microbes in the diverse, fluctuating environments they inhabit, but also for proper development and function of multicellular organisms. In animals, behavioral control is generally achieved
through neural activity. In cells, however, behaviors emerge directly from the joint action of chemical reactions, cellular architecture, and physical mechanisms and constraints within the cell and in its environment. How do cells coordinate the ordered sequences of actions that constitute behaviors in the face of constraints on the precision of sensing and actuation? Despite increasingly sophisticated knowledge of the components of cells, it remains challenging to synthesize this knowledge into understanding of how cells work. To meet this challenge, we are developing tools and approaches for
decoding complex cellular behaviors. Applying these methods to diverse eukaryotic cells, we obtain low dimensional phenotypic spaces in which behaviors can be decomposed into stochastic yet stereotyped sequences of sub-behaviors. This perspective on cell behavior allows us to predict and infer functional physiological states of cells and also to interpret effects of perturbative experiments, yielding mechanistic insights into behavioral control. Altogether, our work sheds new light on how geometry and mechanics encodes cell function. In addition to delivering fundamental insights into the physical and molecular mechanisms and principles of behavioral control in eukaryotic cells, our work stands to enable new ways to predict, guide, and engineer cell function and generate new bio-inspired designs for autonomous control 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.