Continuation methods as a tool for parameter inference in electrophysiology modeling

TL;DR

This paper demonstrates that continuation methods significantly improve the efficiency and robustness of parameter inference in electrophysiology models by reducing computation time and enabling advanced inference techniques.

Contribution

It introduces the application of continuation methods to electrophysiology modeling, showing they outperform traditional simulation approaches in parameter inference tasks.

Findings

Reduces parameter inference computation time by 70%.

Enables robust optimization through multiple starting points.

Facilitates advanced inference methods like MCMC.

Abstract

Parameterizing mathematical models of biological systems often requires fitting to stable periodic data. In cardiac electrophysiology this typically requires converging to a stable action potential through long simulations. We explore this problem through the theory of dynamical systems, bifurcation analysis and continuation methods; under which a converged action potential is a stable limit cycle. Various attempts have been made to improve the efficiency of identifying these limit cycles, with limited success. We demonstrate that continuation methods can more efficiently infer the converged action potential as proposed model parameter sets change during optimization or inference routines. In an example electrophysiology model this reduces parameter inference computation time by 70%. We also discuss theoretical considerations and limitations of continuation method use in place of time-consuming model simulations. The application of continuation methods allows more robust optimization by making extra runs from multiple starting locations computationally tractable, and facilitates the application of inference methods such as Markov Chain Monte Carlo to gain more information on the plausible parameter space.