Data-driven phase-isostable reduction for optimal nonfeedback stabilization of cardiac alternans

Area of Science:

• Computational Neuroscience
• Systems Biology
• Nonlinear Dynamics

Background:

• Standard phase reduction fails for complex nonlinear systems.
• Cardiac alternans are a precursor to life-threatening arrhythmias.
• Model reduction is crucial for understanding and controlling complex biological systems.

Purpose of the Study:

• To develop a data-driven technique for inferring nonlinear system dynamics using phase-isostable reduction.
• To apply this technique to identify control strategies for eliminating cardiac alternans.
• To establish relationships between noise, stimulation, and isostable coordinate variance.

Main Methods:

• Derivation of relationships for cycle-to-cycle variance in reduced isostable coordinates under noise and periodic stimulation.
• Development of a data-driven model inference strategy for the phase-isostable framework.
• Application to cardiac alternans using action potential duration measurements.

Main Results:

• Successfully inferred nonlinear terms of the phase-isostable coordinate reduction framework.
• Identified energy-optimal, nonfeedback control inputs to stabilize a period-1, alternans-free solution.
• Demonstrated the efficacy of the data-driven approach on a biologically relevant problem.

Conclusions:

• Phase-isostable reduction offers a powerful tool for modeling complex nonlinear systems where traditional methods fail.
• The proposed data-driven inference strategy enables effective control of biological rhythms, specifically cardiac alternans.
• This approach provides a pathway to designing targeted interventions for arrhythmias.