Krogh-Madsen Trine, Kold Taylor Louise, Skriver Anne D, Schaffer Peter, Guevara Michael R
Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, New York 10065, USA.
Department of Physiology and Centre for Applied Mathematics in Biology and Medicine, McGill University, Montreal, Quebec H3G 1Y6, Canada.
Chaos. 2017 Sep;27(9):093929. doi: 10.1063/1.5001200.
The transmembrane potential is recorded from small isopotential clusters of 2-4 embryonic chick ventricular cells spontaneously generating action potentials. We analyze the cycle-to-cycle fluctuations in the time between successive action potentials (the interbeat interval or IBI). We also convert an existing model of electrical activity in the cluster, which is formulated as a Hodgkin-Huxley-like deterministic system of nonlinear ordinary differential equations describing five individual ionic currents, into a stochastic model consisting of a population of ∼20 000 independently and randomly gating ionic channels, with the randomness being set by a real physical stochastic process (radio static). This stochastic model, implemented using the Clay-DeFelice algorithm, reproduces the fluctuations seen experimentally: e.g., the coefficient of variation (standard deviation/mean) of IBI is 4.3% in the model vs. the 3.9% average value of the 17 clusters studied. The model also replicates all but one of several other quantitative measures of the experimental results, including the power spectrum and correlation integral of the voltage, as well as the histogram, Poincaré plot, serial correlation coefficients, power spectrum, detrended fluctuation analysis, approximate entropy, and sample entropy of IBI. The channel noise from one particular ionic current (I), which has channel kinetics that are relatively slow compared to that of the other currents, makes the major contribution to the fluctuations in IBI. Reproduction of the experimental coefficient of variation of IBI by adding a Gaussian white noise-current into the deterministic model necessitates using an unrealistically high noise-current amplitude. Indeed, a major implication of the modelling results is that, given the wide range of time-scales over which the various species of channels open and close, only a cell-specific stochastic model that is formulated taking into consideration the widely different ranges in the frequency content of the channel-noise produced by the opening and closing of several different types of channels will be able to reproduce precisely the various effects due to membrane noise seen in a particular electrophysiological preparation.
跨膜电位是从小的等电位簇(由2 - 4个自发产生动作电位的胚胎鸡心室细胞组成)记录得到的。我们分析了连续动作电位之间时间的逐周期波动(心跳间期或IBI)。我们还将现有的簇中电活动模型(该模型被表述为一个类似霍奇金 - 赫胥黎的非线性常微分方程组确定性系统,描述了五种独立的离子电流)转化为一个随机模型,该随机模型由约20000个独立且随机门控的离子通道组成,其随机性由一个真实的物理随机过程(无线电静态噪声)设定。这个使用克莱 - 德费利斯算法实现的随机模型再现了实验中观察到的波动:例如,模型中IBI的变异系数(标准差/均值)为4.3%,而在所研究的17个簇中该平均值为3.9%。该模型还复制了实验结果的其他几个定量指标中的所有指标,除了一个指标外,包括电压的功率谱和相关积分,以及IBI的直方图、庞加莱图、序列相关系数、功率谱、去趋势波动分析、近似熵和样本熵。来自一种特定离子电流(I)的通道噪声,其通道动力学与其他电流相比相对较慢,对IBI的波动起主要作用。通过向确定性模型中添加高斯白噪声电流来再现实验中IBI的变异系数需要使用不切实际的高噪声电流幅度。实际上,建模结果的一个主要启示是,鉴于各种通道开放和关闭的时间尺度范围很广,只有一个考虑到几种不同类型通道开放和关闭所产生的通道噪声频率内容的广泛不同范围而制定的细胞特异性随机模型,才能够精确再现特定电生理制剂中由于膜噪声而看到的各种效应。