Hybrid automata as a unifying framework for modeling excitable cells.

Area of Science:

• Computational Biology
• Mathematical Modeling
• Systems Biology

Background:

• Excitable cells, crucial for physiological functions, are often modeled using complex computational approaches.
• Existing models may lack a unified framework, hindering comparative analysis and development.
• Understanding excitable cell dynamics is vital for various biological and medical applications.

Purpose of the Study:

• To propose hybrid automata (HA) as a unifying framework for computational models of excitable cells.
• To demonstrate the efficacy of HA in approximating nonlinear excitable-cell models.
• To establish HA as a powerful tool for analyzing and representing biological processes.

Main Methods:

• Utilizing hybrid automata (HA), which integrate discrete transition graphs with continuous dynamics.
• Applying HA to approximate the nonlinear dynamics of excitable-cell models.
• Recasting existing computational models (Biktashev's, Fenton-Karma) into the HA framework.

Main Results:

• HA effectively capture action-potential morphology and key excitable-cell characteristics like refractoriness and restitution.
• The Luo-Rudy model of a guinea-pig ventricular myocyte was accurately approximated using HA.
• Established computational models were successfully represented as HA without loss of expressiveness.

Conclusions:

• Hybrid automata provide a versatile and unifying framework for computational modeling of excitable cells.
• HA offer an intuitive graphical representation supported by robust mathematical theory and analysis tools.
• The proposed framework is well-suited for advancing computational biology and understanding biological processes.