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Area of Science:

  • Physics
  • Biophysics
  • Computational Biology

Background:

  • Many natural and societal diffusion processes deviate from standard Brownian motion, exhibiting anomalous diffusion.
  • Biological cell migration is a key example, characterized by memory effects due to slowly decaying velocity autocorrelation functions.

Purpose of the Study:

  • To construct non-Markovian lattice-gas cellular automata models for agents with memory.
  • To investigate anomalous diffusion and memory effects in collective cell dynamics.

Main Methods:

  • Deriving agent reorientation probabilities from a priori specified velocity autocorrelation functions.
  • Utilizing a data-driven approach where correlations dictate agent behavior.
  • Employing cellular automata for computational efficiency.

Main Results:

  • Successfully modeled anomalous diffusion using velocity correlations decaying as power laws.
  • Demonstrated that exponential decay of velocity correlations leads to different diffusion patterns.
  • The models effectively integrate memory effects into agent-based simulations.

Conclusions:

  • The developed models provide a framework for studying memory and anomalous diffusion in interacting cell populations.
  • This approach facilitates exploration of phenomena like confluent cell monolayers and cell clustering.
  • Computational efficiency enables large-scale simulations of complex biological dynamics.