细胞膜离子单通道电流重构的计算机仿真

摘要

细胞膜离子单通道电流十分微弱(PA级),用膜片钳技术测量离子电流往往淹没在强噪声背景中.目前,采用阈值检测方法恢复通道电流信号.但是,通道开放和关闭的电流阈值需要人为设定,并且阈值法在较低信噪比时失效.采用隐马尔可夫模型(HMM)重构离子单通道电流并估计模型参数.对离子通道HMM进行描述和分析;运用Baum-Welch迭代算法训练HMM并估计模型参数;利用Viterbi算法重构通道电流最佳状态序列.将HMM与阈值法进行比较,对不同信噪比和不同转移概率情况下HMM恢复算法进行计算机仿真.结果表明:HMM与阈值法相比,具有较强抗噪能力.在较低信噪比情况下,该模型恢复信号精度高,参数收敛速度快,且电流重构误差主要出现在状态突变点.%Single ion channel current signal of cell membrane is a stochastic ionic current in the order of picoampere(PA).The background noise always dominates in the patch-clamp recordings.At present, the threshold detection method is used to restore channel current signal. However,the current threshold need be setted artificially,and this method cannot work satisfactorily when signal-to-noise ratio is lower. Hidden Markov Model(HMM) is adopted for restoring the ion-channel current and estimating parameters of the model. HMM on ion-channel is described and analyzed. Iterative algorithm based Baum-Welch is used for training HMM and estimating model parameters. Viterbi algorithm is adopted for restoring the best state sequence of ion-channel currents. Comparing HMM with the threshold detection method,the algorithm based HMM is simulated under the different transition probability and signal-to-noise ratio. The experimental results have shown that compared with the threshold detector, HMM has strong ability of anti-noise, high restoration precision, and fast convergent rate under the low signal-tonoise ratio. Moreover,the restored error appears mainly on the mutational points of the current signal.