Advanced Chemical Computing Using Discrete Turing Patterns in Arrays of Coupled Cells

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.