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来提高我们对正常和麻醉剂修改的GABA(A)R功能的理解。GABA(A)受体负责中枢神经系统的大部分快速抑制性神经传递，是多种全身麻醉药的靶标。尽管GABA(A)受体在正常和临床改变的神经功能中都很重要，但对于初级或麻醉剂修饰的受体动力学，还没有一致的计算模型。描述GABA(A)受体的计算模型对于预测突触事件的时间进程、受体的结构转变以及了解分子(如麻醉药)的结合如何改变这些性质是非常有价值的。为了得到受体功能的动力学模型，将比较局部和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。与广泛使用的局部优化方法相比，全局优化方法有望以更高的准确度和精密度来估计动力学模型。然后，最有效的优化方法将被用于根据实验宏观电流对各种刺激方案的响应来估计正常和麻醉剂修改的GABA(A)R的动力学模型。预计更复杂的脉冲方案将揭示GABA(A)R的动力学特征，而这些特征在传统的STEP方案中是不可见的。GABA(A)R模型随后将通过与实验单通道活性的拟合进行改进。预计将存在一个离散的、重叠的模型空间，在其中可以预测宏观和单通道的活动，并且可以在其中推导出GABA(A)R函数的综合动力学模型。在这些目标的结论下，动力学模型将有显著的改进，因此，我们对正常和麻醉修饰的GABA(A)R功能的理解都将得到显著的改进。这些模型将导致对麻醉药如何改变GABA(A)R的动力学和结构转变的预测，并可以被纳入细胞模型，以进一步了解麻醉药如何改变神经活动。因此，这项提议的结果可能最终会提出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 .