Comprehensive kinetic modeling of GABA(A) receptor activation and modulation

描述（由申请人提供）：本提案的总体目标是使用一套全面的实验和计算建模技术，以提高我们对正常和麻醉修饰的GABA（A）R功能的理解。 GABA（A）Rs负责中枢神经系统中的大多数快速抑制性神经传递，并且是多种全身麻醉剂的靶标。尽管GABA（A）受体在正常和临床改变的神经功能中的重要性，但对于初级或麻醉修饰的受体动力学，还不存在一致的计算模型。描述GABA（A）受体的计算模型在预测突触事件的时间过程、受体的结构转变以及理解这些性质如何被分子（如麻醉剂）的结合改变方面是非常宝贵的。为了获得受体功能的动力学模型，将比较局部与全局优化方法从已知模拟模型的宏观和单通道电流估计动力学模型的能力。全局优化方法有望以比广泛使用的局部方法更高的准确度和精度来估计动力学模型。最有效的优化方法，然后将被用来估计动力学模型的正常和麻醉剂修饰的GABA（A）R从实验宏观电流响应各种刺激协议。预计更复杂的脉冲方案将揭示传统步骤方案不可见的GABA（A）R的动力学特征。 GABA（A）R模型将通过拟合实验单通道活动进行改进。预计将存在一个离散的、重叠的模型空间，在该空间中预测宏观和单通道活动，并且可以导出GABA（A）R功能的一个综合动力学模型。在这些目标的结论，显着的改进将已作出的动力学模型，因此，我们的理解正常和麻醉修饰的GABA（A）R功能。这些模型将导致麻醉剂如何改变GABA（A）R的动力学和结构转变的预测，并可以纳入细胞模型，以进一步了解麻醉剂如何改变神经活动。 因此，该提案的结果可能最终建议更好的方法来控制麻醉状态 .

DESCRIPTION (provided by applicant): The overall objective of this proposal is to use a comprehensive set of experimental and computational modeling techniques to improve our understanding of normal and anesthetic-modified GABA(A)R function. GABA(A)Rs are responsible for the majority of fast inhibitory neural transmission in the central nervous system and are the target for a variety of general anesthetics. Despite the importance of GABA(A)Rs in both normal and clinical-altered neural function, no consensus computational model exists for either the primary or the anesthetic-modified receptor kinetics. Computational models that describe GABA(A)Rs are invaluable in predicting the time course of synaptic events, the structural transitions of the receptor and in understanding how these properties are altered by the binding of molecules, such as anesthetics. To derive kinetic models of receptor function, local versus global optimization methods will be compared for their ability to estimate a kinetic model from macroscopic and single-channel currents of known, simulated models. The global optimization methods are expected to estimate a kinetic model with greater accuracy and precision than the widely-used local methods. The most efficient optimization method will then be used to estimate kinetic models for the normal and anesthetic-modified GABA(A)R from experimental macroscopic current responses to a variety of stimulus protocols. More complex pulse protocols are expected to unmask kinetic features of the GABA(A)R that are not visible with traditional step protocols. GABA(A)R models will then be refined by fitting to experimental single-channel activity. It is expected that a discrete, overlapping model space will exist, in which both macroscopic and single-channel activity are predicted and in which one comprehensive kinetic model of GABA(A)R function can be derived. At the conclusion of these aims, significant improvements will have been made to the kinetic models and, therefore, to our understanding of both normal and anesthetic-modified GABA(A)R function. These models will lead to predictions of how anesthetics alter the kinetics and structural transitions of GABA(A)R and can be incorporated into cellular models to further our understanding of how anesthetics alter neural activity. Thus, the results of this proposal may ultimately suggest better methods to control the state of anesthesia .