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首页> 美国卫生研究院文献>Biophysical Journal >Stochastic Model of Gap Junctions Exhibiting Rectification and Multiple Closed States of Slow Gates
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Stochastic Model of Gap Junctions Exhibiting Rectification and Multiple Closed States of Slow Gates

机译：间隙栅的整流与慢闸多重闭合状态的随机模型

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Gap-junction (GJ) channels formed from connexin (Cx) proteins provide direct pathways for electrical and metabolic cell-cell communication. Earlier, we developed a stochastic 16-state model (S16SM) of voltage gating of the GJ channel containing two pairs of fast and slow gates, each operating between open (o) and closed (c) states. However, experimental data suggest that gates may in fact contain two or more closed states. We developed a model in which the slow gate operates according to a linear reaction scheme, o↔c1↔c2, where c1 and c2 are initial-closed and deep-closed states that both close the channel fully, whereas the fast gate operates between the open state and the closed state and exhibits a residual conductance. Thus, we developed a stochastic 36-state model (S36SM) of GJ channel gating that is sensitive to transjunctional voltage (Vj). To accelerate simulation and eliminate noise in simulated junctional conductance (gj) records, we transformed an S36SM into a Markov chain 36-state model (MC36SM) of GJ channel gating. This model provides an explanation for well-established experimental data, such as delayed gj recovery after Vj gating, hysteresis of gj-Vj dependence, and the low ratio of functional channels to the total number of GJ channels clustered in junctional plaques, and it has the potential to describe chemically mediated gating, which cannot be reflected using an S16SM. The MC36SM, when combined with global optimization algorithms, can be used for automated estimation of gating parameters including probabilities of c1↔c2 transitions from experimental gj-time and gj-Vj dependencies.
机译：由连接蛋白（Cx）蛋白质形成的间隙连接（GJ）通道为电和代谢细胞之间的通信提供了直接途径。先前，我们开发了GJ通道电压门控的随机16状态模型（S16SM），其中包含两对快速和慢速门，每对门在打开（o）和关闭（c）状态之间运行。但是，实验数据表明，闸门实际上可能包含两个或多个闭合状态。我们开发了一个模型，其中慢速浇口根据线性反应方案o↔c1↔c2进行操作，其中c1和c2是初始关闭状态和深度关闭状态，均会完全关闭通道，而快速浇口在两个方向之间运行打开状态和关闭状态，并表现出残余电导。因此，我们开发了对跨结电压（Vj）敏感的GJ通道门控的随机36状态模型（S36SM）。为了加快仿真速度并消除模拟的连接电导（gj）记录中的噪声，我们将S36SM转换为GJ通道门控的Markov链36状态模型（MC36SM）。该模型为公认的实验数据提供了解释，例如，Vj门控后延迟的gj恢复，gj-Vj依赖性的滞后现象以及功能通道与聚集在斑块中的GJ通道总数之比低，它具有描述化学介导的门控的潜力，这无法使用S16SM反映出来。当与全局优化算法结合使用时，MC36SM可用于自动估计门控参数，包括从实验gj-time和gj-Vj依赖关系得出c1↔c2跃迁的概率。

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