Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding of such cooperativity remains elusive, primarily due to limitations in measuring separately the response of each channel. Rather, only the superimposed channel response can be observed, challenging existing data analysis methods. To address this gap, we propose IDC (Idealisation, Discretisation, and Cooperativity inference), a robust statistical data analysis methodology that requires only voltage-clamp current recordings of an ensemble of ion channels. The framework of IDC enables us to integrate recent advancements in idealisation techniques and coupled Markov models. Further, in the cooperativity inference phase of IDC, we introduce a minimum distance estimator and establish its statistical guarantee in the form of asymptotic consistency. We demonstrate the effectiveness and robustness of IDC through extensive simulation studies. As an application, we investigate gramicidin D channels. Our findings reveal that these channels act independently, even at varying applied voltages during voltage-clamp experiments. An implementation of IDC is available from GitLab.

翻译：近期实验研究揭示了离子通道可能表现出协同行为这一引人入胜的可能性。然而，由于无法独立测量每个通道的响应，对这种协同行为的全面理解仍然难以实现。相反，我们只能观察到叠加后的通道响应，这对现有数据分析方法构成了挑战。为弥补这一不足，我们提出了IDC（理想化、离散化与协同性推断），一种仅需离子通道集合的电压钳电流记录即可实现的稳健统计数据分析方法。IDC框架使我们能够整合理想化技术的最新进展与耦合马尔可夫模型。此外，在IDC的协同性推断阶段，我们引入了一个最小距离估计量，并以其渐近一致性形式建立了统计保证。通过大量模拟研究，我们证明了IDC的有效性和稳健性。作为应用实例，我们对短杆菌肽D通道进行了研究。结果表明，即便在电压钳实验中施加变化的电压，这些通道仍独立运作。IDC的实现代码已托管于GitLab。