Stochastic Modeling of Ion Channels

离子通道的随机建模

• 批准号: 9625384
• 负责人: John Rice
• 金额: $ 22.5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-15 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9625384&HistoricalAwards=false
• 关键词: Stochastic; Modeling; Ion; Channels

9625384 Rice and Fredkin ABSTRACT The investigators do research on a number of problems that arise in modeling the stochastic behavior of ion channels and in the statistical analysis of patch clamp recordings: (1) investigations of maximum likelihood estimation of kinetic parameters with particular attention to computational aspects and the interpretation and assessment of the kinetic models; (2) investigation of problems which arise in the analysis of records in which multiple channels are present; (3) investigation of the utility of models which are based on stochastic differential equations in contrast to Markov models with a discrete state space, which have typically been used in the field. Ion channels are large proteins that allow current in the form of ions to pass across cell membranes. These channels are the fundamental units of current conduction in the neuromuscular system and the interrelationships of a large variety of ion channels shape the electrical signals of the system. The study of ion channels is useful not only for the purpose of deeper basic understanding of the neuromuscular system, but also because ion channel function is implicated in various neuromuscular diseases and because pharmaceutical products, such as anesthetics, are targeted at ion channels. An experimental technique known as the "single channel patch clamp" allows recordings to be made which provide important information about these channels. The apparently random character of the recorded currents has led to the widespread use of statistical models for the dynamics of the channels and to the use of statistical techniques of data processing. The very large quantity of data produced by such recordings poses substantial computational problems which must be resolved in order that important information about the channels can be extracted efficiently. This interdisciplinary proposal is primarily concerned with the development of effective statistical and computational methodology.

摘要研究人员对离子通道随机行为建模和膜片钳记录统计分析中出现的一些问题进行了研究：(1)研究动力学参数的最大似然估计，特别关注计算方面以及动力学模型的解释和评估；（二）对多渠道记录分析中出现的问题进行调查；(3)研究基于随机微分方程的模型与具有离散状态空间的马尔可夫模型的效用，后者通常在该领域使用。离子通道是允许电流以离子形式通过细胞膜的大型蛋白质。这些离子通道是神经肌肉系统中电流传导的基本单位，各种离子通道的相互关系形成了该系统的电信号。离子通道的研究不仅对神经肌肉系统有更深入的基础了解，而且还因为离子通道的功能与各种神经肌肉疾病有关，而且因为药物产品，如麻醉剂，都是针对离子通道的。一种被称为“单通道膜片钳”的实验技术允许录音，提供关于这些通道的重要信息。所记录的电流的明显的随机特性导致了对渠道动力学的统计模型的广泛使用和对数据处理的统计技术的使用。这种记录产生的大量数据带来了大量的计算问题，必须解决这些问题，以便有效地提取有关信道的重要信息。这个跨学科的建议主要涉及发展有效的统计和计算方法。