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Lenia, a cellular automata framework, models cancer dynamics, revealing how short-range interactions promote survival and asymmetric tumor-immune responses impair immunity. Collagen patterns offer immune protection, inversely related to disease stage.

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Area of Science:

  • Computational Biology
  • Artificial Life
  • Cancer Research

Background:

  • Cancer progression models traditionally use agent-based approaches with local rules for global dynamics.
  • Existing models often struggle to integrate diverse cancer features like morphogenesis and adaptation.
  • Cellular automata offer a flexible framework for complex biological system modeling.

Purpose of the Study:

  • To implement mathematical cancer models within the Lenia cellular automata framework.
  • To explore local (cell-scale) to global (tumor-scale) dynamics using interaction kernels.
  • To develop data-informed models of cancer growth, competition, and migration.

Main Methods:

  • Utilized Lenia, a cellular automata framework, for implementing cancer models.
  • Defined interaction kernels to govern density-dependent growth dynamics.
  • Developed models for single-population growth, multi-population competition, and cell migration/chemotaxis.

Main Results:

  • Short-range interaction kernels were found to enable tumor cell survival under Allee effects.
  • Asymmetric tumor-immune interaction kernels correlated with diminished immune response.
  • Modeled immune-extracellular matrix interactions, showing collagen patterns confer immune protection.

Conclusions:

  • Lenia provides a versatile platform for diverse cancer modeling approaches (local/global, deterministic/stochastic, etc.).
  • Mechanistic insights reveal survival advantages from short-range interactions and disadvantages from asymmetric tumor-immune responses.
  • Emergent patterns in collagen formation suggest a protective role against cancer progression, inversely correlating with immune coverage.