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Proliferation of cells with aggregation and communication
1Section of Biological Physics, Department of Physics, Chalmers University of Technology, Göteborg, Sweden; Boreskov Institute of Catalysis, Russian Academy of Sciences, Novosibirsk, Russia.
Mathematical Biosciences
|February 3, 2018
Summary
Cell aggregation significantly alters proliferation dynamics, challenging the standard Fisher-Kolmogorov model. Simulations show adhesion impacts cell division rates and front propagation, leading to variable velocities and smeared fronts.
Area of Science:
- Mathematical Biology
- Cell Biology
- Computational Biology
Background:
- Cell proliferation is often modeled using reaction-diffusion equations, assuming constant velocity front propagation (Fisher-Kolmogorov-Petrovsky-Piskunov model).
- This model overlooks crucial biological factors like cell-cell adhesion and communication, which can influence proliferation kinetics.
Purpose of the Study:
- To investigate the impact of cell adhesion and cell-cell communication on cell proliferation dynamics.
- To explore deviations from the standard Fisher-Kolmogorov model predictions using advanced simulation techniques.
Main Methods:
- Extensive Monte Carlo simulations were employed to model cell proliferation.
- The simulations incorporated cell aggregation due to adhesion and the influence of cell-cell communication on division rates.
Main Results:
- Cell adhesion was found to dramatically modify spatio-temporal proliferation kinetics.
- The conventional relationship between front velocity and diffusion coefficient was shown to fail.
- Observed phenomena include increasing front velocity over time and partial or complete smearing of the proliferation front on realistic scales.
Conclusions:
- Cellular aggregation and communication are critical factors that significantly alter cell proliferation dynamics.
- The standard Fisher-Kolmogorov model may not accurately capture real-world cell proliferation under conditions of adhesion and communication.
- Monte Carlo simulations provide a valuable tool for exploring complex biological processes beyond simplified theoretical frameworks.
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