Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling

DOI10.1016/j.mbs.2024.109158zbMATH Open1539.92024MaRDI QIDQ6550776

Kyle C. Nguyen, Jason M. Haugh, Ralph C. Smith, John T. Nardini, Carter D. Jameson, Scott A. Baldwin, Kevin B. Flores

Publication date: 5 June 2024

Published in: Mathematical Biosciences (Search for Journal in Brave)

zbMATH Keywords

approximate Bayesian computationtopological data analysiscell migrationagent-based modelingdeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Cell movement (chemotaxis, etc.) (92C17) Topological data analysis (62R40)

Cites Work

• Unnamed Item
• Ripser: efficient computation of Vietoris-Rips persistence barcodes
• Diatomic molecules according to the wave mechanics. II: Vibrational levels.
• Continuum approximations for lattice-free multi-species models of collective cell migration
• State transitions and the continuum limit for a 2D interacting, self-propelled particle system
• An Evolutionary Model of Tumor Cell Kinetics and the Emergence of Molecular Heterogeneity Driving Gompertzian Growth
• Clique topology reveals intrinsic geometric structure in neural correlations

Related Items (2)

How cells stay together: a mechanism for maintenance of a robust cluster explored by local and non-local continuum models ⋮ Forecasting and predicting stochastic agent-based model data with biologically-informed neural networks

This page was built for publication: Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling