Chaotic model and memory in single calcium-activated potassium channel kinetics

被引:15
|
作者
Bandeira, Heliovanio T. [2 ]
Barbosa, Catao T. F. [3 ]
De Oliveira, Regina A. Campos [1 ]
Aguiar, Jose F. [4 ]
Nogueira, Romildo A. [1 ]
机构
[1] Univ Fed Rural Pernambuco, Dept Anim Morphol & Physiol, BR-52171030 Recife, PE, Brazil
[2] Univ Fed Rural Pernambuco, Dept Stat & Informat, BR-52171030 Recife, PE, Brazil
[3] Univ Fed Pernambuco, Dept Phys Educ, BR-50670901 Recife, PE, Brazil
[4] Univ Sao Paulo, Dept Physiol, Sch Med, BR-14049900 Ribeirao Preto, SP, Brazil
关键词
D O I
10.1063/1.2944980
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Ion channels are pores formed by proteins and responsible for carrying ion fluxes through cellular membranes. The ion channels can assume conformational states thereby controlling ion flow. Physically, the conformational transitions from one state to another are associated with energy barriers between them and are dependent on stimulus, such as, electrical field, ligands, second messengers, etc. Several models have been proposed to describe the kinetics of ion channels. The classical Markovian model assumes that a future transition is independent of the time that the ion channel stayed in a previous state. Others models as the fractal and the chaotic assume that the rate of transitions between the states depend on the time that the ionic channel stayed in a previous state. For the calcium activated potassium channels of Leydig cells the R/S Hurst analysis has indicated that the channels are long-term correlated with a Hurst coefficient H around 0.7, showing a persistent memory in this kinetic. Here, we applied the R/S analysis to the opening and closing dwell time series obtained from simulated data from a chaotic model proposed by L. Liebovitch and T. Toth [J. Theor. Biol. 148, 243 (1991)] and we show that this chaotic model or any model that treats the set of channel openings and closings as independent events is inadequate to describe the long-term correlation (memory) already described for the experimental data. (C) 2008 American Institute of Physics.
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页数:6
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