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Understanding interactions between populations: Individual based modelling and quantification using pair correlation

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Area of Science:

  • Ecology, developmental biology, and regenerative medicine.
  • Mathematical modeling of biological systems.
  • Collective behavior and pattern formation.

Background:

  • Swarming and aggregation patterns are crucial in various biological fields.
  • Existing models often struggle to represent discrete entities like cells and animals.
  • The behavior of two interacting species is less understood than single-species systems.

Purpose of the Study:

  • To develop a discrete model for simulating two-species interactions.
  • To identify robust spatial distribution features in agent-based models.
  • To introduce a quantitative tool for analyzing multi-species patterns.

Main Methods:

  • Development of an agent-based model simulating attraction and repulsion between two types of individuals.
  • Analysis of simulation outputs to identify persistent spatial features.
  • Introduction and application of a pair correlation function (PCF) for quantitative analysis.

Main Results:

  • Agent-based modeling successfully simulates diverse spatial patterns arising from inter-individual interactions.
  • The pair correlation function (PCF) effectively quantifies spatial distributions.
  • Differing interaction strengths between species lead to distinct patterns quantifiable by PCF.

Conclusions:

  • The agent-based model provides a framework for studying discrete multi-species interactions.
  • The PCF is a valuable tool for quantitatively analyzing complex spatial patterns.
  • This approach aids in comparing simulation data with experimental observations in biology.