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Modeling selective local interactions with memory.

Amanda Galante1, Doron Levy

  • 1Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD 20742.

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This study models cyanobacteria group dynamics using a new mathematical approach. Numerical simulations reveal how individual cell choices and memory influence aggregation behavior and stability.

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collective motiongroup dynamicsphototaxisreaction-diffusion master equation

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Area of Science:

  • Microbiology
  • Mathematical Biology
  • Biophysics

Background:

  • Cyanobacteria, like *Synechocystis* sp., exhibit group dynamics and phototaxis for light seeking.
  • Previous models captured aggregation and random motion but lacked focus on individual cell choices.

Purpose of the Study:

  • To enhance understanding of cyanobacteria group dynamics.
  • To investigate the role of selective neighbor interaction and motion persistence.

Main Methods:

  • Developed a stochastic particle system for cyanobacteria interactions.
  • Replaced the 2D stochastic model with a 1D system of ordinary differential equations.
  • Incorporated selective neighbor choice and motion memory into the model.

Main Results:

  • Numerical simulations explored parameter space efficiently.
  • Analyzed the stability, size, and merging dynamics of cell aggregations.
  • The model replicates experimentally observed phenomena like aggregation and quasi-random motion.

Conclusions:

  • The new model provides insights into the mechanisms driving cyanobacteria collective behavior.
  • Selective interactions and persistent motion are key factors in aggregation formation and stability.