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Dynamic cluster field modeling of collective chemotaxis
Aditya Shankar Paspunurwar1, Adrian Moure2, Hector Gomez3,4,5
1School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, 47907, IN, USA.
Scientific Reports
|October 25, 2024
Summary
A new computational method, Dynamic Cluster Field modeling (DCF), simulates large-scale collective cell migration and chemotaxis. This approach accurately models chemoattractant dynamics, advancing our understanding of crucial biological processes.
Area of Science:
- Computational biology
- Cellular dynamics
- Biophysics
Background:
- Collective cell migration, guided by chemotaxis, is vital for development and disease.
- Accurate modeling is challenging due to cell-cell interactions and chemoattractant regulation.
- Existing models are limited to small cell numbers.
Purpose of the Study:
- Introduce Dynamic Cluster Field modeling (DCF) for large-scale collective cell migration simulations.
- Enable high-resolution modeling of chemoattractant dynamics in evolving extracellular environments.
- Validate the DCF method against experimental data.
Main Methods:
- Developed Dynamic Cluster Field modeling (DCF) for simulating systems with many cells.
- Incorporated high-resolution chemoattractant transport dynamics.
- Validated simulations against experimental scenarios including secretion, uptake, confinement, and enzyme interactions.
Main Results:
- DCF successfully simulates collective chemotaxis in large cellular systems.
- The model accurately captures chemoattractant dynamics influenced by cell behavior and environment.
- Numerical simulations align with experimental observations across diverse biological contexts.
Conclusions:
- DCF is an efficient and predictive computational tool for studying collective cell migration.
- This method facilitates research into complex biological processes like cancer metastasis and neural development.
- Opens new avenues for investigating cell-environment interactions in biological systems.
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