Dynamical system framework suggest first-order phase transition between morphology-based cell states

Recorded on 11/04/2025
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Title: A dynamical systems framework applied to live cell imaging suggests a first-order phase transition between morphology-based cell states
Speaker: Susanne Rafelski
Abstract: Efforts to quantify a Waddington-like landscape from the perspective of dynamical systems have largely focused on describing cell states from the molecular perspective such as via gene regulatory networks. Here we, instead, use morphological data to quantify stable cell states. We take advantage of the ability of human induced pluripotent stem cell-derived endothelial-like cells (hiPSC-ECs) to undergo an inducible cell state transition as they change their alignment behavior (cell organization and migration) in relation to the direction of flow under low and high fluid shear stress (FSS),
respectively. We developed a segmentation-free unsupervised machine learning (ML) approach to extract features from the transmitted light images from timelapse videos of hiPSC-ECs expressing GFP-tagged VE-Cadherin under different FSS. We applied a dynamical systems framework to these features and constructed vector field representations of the cell state dynamics, from which we identified stable fixed points at varying levels of FSS. We found that the way these attractors change with FSS suggests a first-order phase transition. We validated biological interpretation of these
results by using the ML model to reconstruct representative images of the VE-Cadherin channel at these fixed points. By further comparison with more standard cell-centric approaches including segmentation-based individual cell trajectories and their metrics (eg., cell density and alignment), we found that cells take varied paths towards the fixed points. Our work acts as a proof-of-concept framework for quantifying the non-equilibrium driving forces of cell state switching and response to perturbation from
microscopy image-based data.

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.