标题： 连续方法作为电生理学建模中参数推理的工具 显示英文标题

标题： Continuation methods as a tool for parameter inference in electrophysiology modeling

摘要： 参数化生物系统的数学模型通常需要拟合稳定的周期性数据。在心脏电生理学中，这通常需要通过长时间模拟来收敛到一个稳定的动作电位。我们通过动力系统理论、分岔分析和连续方法来探讨这个问题；在这种情况下，收敛的动作电位是一个稳定的极限环。已经有许多尝试来提高识别这些极限环的效率，但收效有限。我们证明了连续方法可以在优化或推理过程中，当所提出模型参数集发生变化时，更有效地推断出收敛的动作电位。在一个电生理学模型的例子中，这将参数推理计算时间减少了70%。我们还讨论了在代替耗时的模型模拟时，连续方法的理论考虑和局限性。连续方法的应用使得从多个起始位置进行额外运行在计算上变得可行，从而促进了推理方法（如马尔可夫链蒙特卡洛）的应用，以获得更多关于可能参数空间的信息。 显示英文摘要

摘要： 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.