To advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) simulations and Markov state models (MSMs) has enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increases exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called independent Markov decomposition (IMD) that leverages weak coupling between subsystems to compute a global kinetic model without requiring the sampling of all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD models can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.

为了推进计算机细胞生物学的使命，对大型复杂生物系统的相互作用进行建模变得越来越重要。分子动力学（MD）模拟和马尔可夫状态模型（MSM）的结合使得能够在长时间尺度上构建简化的分子动力学模型。尽管取得了成功，但这种方法本质上受到分子系统大小的限制。随着大分子复合物尺寸的增加，独立或弱耦合子系统的数量增加，全局系统状态的数量呈指数级增加，使得对所有不同的全局状态进行采样变得不可行。在这项工作中，我们提出了一种称为独立马尔可夫分解（IMD）的技术，它利用子系统之间的弱耦合来计算全局动力学模型，而不需要对子系统的所有组合状态进行采样。我们给出了 IMD 的理论基础，并提出了一种寻找和验证这种分解的方法。使用电生理学中成熟的离子通道模型的经验少态 MSM，我们证明 IMD 模型可以重现实验电导测量，与标准 MSM 方法相比，采样次数大幅减少。我们进一步展示了如何找到全原子蛋白质模拟到弱耦合亚基的最佳分配。