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Area of Science:

  • Chemical kinetics and reaction-diffusion systems
  • Nonlinear dynamics and pattern formation
  • Computational chemistry and chemical computing

Background:

  • Discrete Turing patterns can emerge in reaction-diffusion systems.
  • Chemical computing offers an alternative to traditional electronic computation.
  • The glycolytic oscillator is a well-studied biochemical system exhibiting complex dynamics.

Purpose of the Study:

  • To investigate dynamical switching among discrete Turing patterns in mass-coupled reaction cells.
  • To explore the potential of these patterns for chemical computing applications.
  • To design cellular assemblages for advanced chemical computing using glycolytic models.

Main Methods:

  • Analysis of mass-coupled reaction cells in various topological configurations (linear, cyclic, branched arrays).
  • Modeling the glycolytic reaction using an inhibitor-activator model with ADP (activator) and ATP (inhibitor).
  • Employing stability and bifurcation analysis to identify conditions for Turing patterns and control switching between states.

Main Results:

  • Identified conditions for stable symmetric and asymmetric discrete Turing patterns coexisting with uniform periodic oscillations.
  • Demonstrated the ability to switch between coexisting stable regimes using targeted perturbations.
  • Developed logic gates based on array topology and pattern switching for chemical computing.

Conclusions:

  • Discrete Turing patterns in coupled reaction cells can be dynamically controlled for chemical computing.
  • Array topology and transport regimes significantly influence pattern formation and computation.
  • The proposed cellular assemblage design integrates chemical computing with glycolytic excitable channels for advanced functionalities.