Cartoon Room 2 (#3147)

Data-driven, modeling, and topological techniques in cell and developmental biology

Tuesday, July 18 at 10:30am

SMB2023 SMB2023 Follow Tuesday during the "MS03" time block.
Room assignment: Cartoon Room 2 (#3147).
Note: this minisymposia has multiple sessions. The other session is MS04-CDEV-1 (click here).

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Alexandria Volkening, Andreas Buttenschoen, Veronica Ciocanel


Cell and developmental biology spans many interconnected temporal and spatial scales, including carefully orchestrated genetic regulatory networks and other intracellular dynamics, interactions between pairs of cells, and the collective behavior of thousands of cells during tissue formation or disease-related tissue disruption. Whether focused on questions such as how cells make decisions about their fate, respond to external signals, regulate their shape, migrate, or communicate with one another, the mathematical methods that researchers develop to address these questions are similarly broad and interconnected. Motivated by these observations, our minisymposium brings together scientists addressing a wide range of biological questions using data-driven approaches, mathematical modeling, or topological techniques. Our goal is to showcase mathematically complementary approaches and highlight interconnected questions in cell and developmental biology.

Dhananjay Bhaskar

Yale University (Department of Genetics)
"Analyzing Spatiotemporal Signaling Patterns using Geometric Scattering and Persistent Homology"
Cells communicate with one another through a variety of signaling mechanisms. The exchange of information via these mechanisms allows cells to coordinate their behavior and respond to environmental stress and other stimuli. To facilitate quantitative understanding of complex spatiotemporal signaling activity, we developed Geometric Scattering Trajectory Homology (GSTH), a general framework that encapsulates time-lapse signals on a cell adjacency graph in a low-dimensional trajectory. GSTH captures the signal at multiple spatial scales and over time by applying manifold-geometry preserving dimensionality reduction to geometric scattering features obtained from a cascade of graph wavelet filters. We tested this framework using computational models of collective oscillations and calcium signaling in the Drosophila wing imaginal disc, as well as experimental data, including in vitro ERK signaling in human mammary epithelial cells and in vivo calcium signaling from the mouse epidermis and visual cortex. We found that the geometry and topology of the trajectory are related to the degree of synchrony (over space and time), intensity, speed, and quasi-periodicity of the signaling pattern. We recovered model parameters and experimental conditions by training neural networks on trajectory data, showing that our approach preserves information that characterizes various cell types, tissues, and drug treatments. We envisage the applicability of our framework in various biological contexts to generate new insights into cell communication.
Additional authors: Jessica Moore, Yale University; Feng Gao, Columbia University; Bastian Rieck, Technical University of Munich; Firas Khasawneh, Michigan State University; Elizabeth Munch, Michigan State University; Valentina Greco, Yale University; Smita Krishnaswamy, Yale University

Keisha Cook

Clemson University (School of Mathematical and Statistical Sciences)
"Predictive Modeling of the Cytoskeleton"
Biological systems are traditionally studied as isolated processes (e.g. regulatory pathways, motor protein dynamics, transport of organelles, etc.). Although more recent approaches have been developed to study whole cell dynamics, integrating knowledge across biological levels remains largely unexplored. In experimental processes, we assume that the state of the system is unknown until we sample it. Many scales are necessary to quantify the dynamics of different processes. These may include a magnitude of measurements, multiple detection intensities, or variation in the magnitude of observations. The interconnection between scales, where events happening at one scale are directly influencing events occurring at other scales, can be accomplished using mathematical tools for integration to connect and predict complex biological outcomes. In this work we focus on building statistical inference methods to study the complexity of the cytoskeleton from one scale to another by relying on two main components facilitating intracellular transport; that is microtubule network organization and cargo transport.
Additional authors: Scott McKinley, Tulane University; Christine Payne, Duke University; Nathan Rayens, Duke University

Calina Copos

Northeastern University (Biology and Mathematics)
"From microscopy to the distribution of mechanochemical efforts across a pair of cells"
In a model organism, we use a combination of mathematical and experimental tools to tease apart the distribution of forces in a pair of cells responsible for forming the heart (and the pharynx). The heart progenitors provide one of the simplest examples of collective cell migration whereby just two cells migrate with a defined leader-trailer “assignment” between two tissues. The cells are also capable of moving individually, albeit by a shorter distance, with imperfect directionality, and with altered morphology. Thus, maintaining contact and the leader-trailer roles is important for directed migration to the destination. However, it is unclear why a two-cell system is better at migration than an individual cell. Based on in-vivo fluorescence imaging of the embryo, we obtain morphological measurements of the cells. Borrowing on the formulation of active droplet theory, we extract intracellular pressure and forces at the intersection of interfaces (e.g. cell-cell, cell-surface, cell-environment). These extracted measurements are then tested in the active droplet pair framework, and we observe that the 2-cell system does migrate and migrate persistently due to the difference in contact angle at the leading of the leader cell and the trailing edge of the rear cell.
Additional authors: Selena Gupta, New York University; Yelena Bernadskaya, New York University; Alex Mogilner, Courant Institute, New York University

Daniel A. Cruz

University of Florida (Department of Medicine)
"Topological data analysis of pattern formation in stem cell colonies"
Confocal microscopy imaging provides both positional information and expression levels from in vitro cell cultures; however, few methods exist to quantify the spatial organization of such cultures. Current quantitative tools generally rely on human annotation, require the a priori selection of parameters, or potentially lack biological interpretability. To address these limitations, we develop a modular, general-purpose pipeline that uses topological data analysis to extract structural summaries from cellular patterns at multiple scales. We apply our pipeline to study the pattern formation of human induced pluripotent stem cell (hiPSC) cultures, which have become powerful, patient-specific test beds for investigating the early stages of embryonic development. Our analysis captures both subtle differences in the spatial organization of hiPSCs based on different biological markers and progressive changes in patterning across incremental modifications of certain experimental conditions. These results imply the existence of directed cellular movement and morphogen-mediated, neighbor-to-neighbor signaling in the context of hiPSC differentiation.
Additional authors: Iryna Hartsock, University of Florida; Eunbi Park, Georgia Institute of Technology; Jack Toppen, Georgia Institute of Technology; Elena S. Dimitrova, California Polytechnic State University, San Luis Obispo; Peter Bubenik, University of Florida; Melissa L. Kemp, Georgia Institute of Technology and Emory University

Hosted by SMB2023 Follow
Annual Meeting for the Society for Mathematical Biology, 2023.