Explore data-driven modeling and topological data analysis (TDA) of self-organized multicellular architectures in this comprehensive lecture. Delve into the application of TDA and machine learning for automated classification of multicellular structures in cancer EMT and embryonic development. Learn about characterizing epithelial migration phases, computing persistent homology, and using unsupervised classification for topological features. Discover how this model-agnostic approach can provide quantitative insights into complex tissue topology emergence through spatiotemporal cell interactions. Examine simulation results, parameter sweeps, phase diagrams, and experimental findings. Gain understanding of limitations, ongoing work, and future directions in this field of study.
Data-Driven Modeling & TDA of Self-Organized Multicellular Architectures
Applied Algebraic Topology Network via YouTube
Overview
Syllabus
Introduction
Outline
Etiology to Mesenchymal Transition
Confocal Microscopy
Cell Segmentation
Cell Model
Simulation Results
Parameters
Parameter sweeps
TDA
Phase Diagram
Proliferation to Robustness
Average Persistence Diagram
Robustness
Nondimensional parameters
Summary
Limitations
Experiment
Experimental Results
Previous strategy
Persistence images
Simulations
Unsupervised Classification
Results
Discussion
Conclusion
Ongoing work
Future work
Future directions
Taught by
Applied Algebraic Topology Network
