High Throughput Mapping of Neuronal and Glial Networks

描述（由申请人提供）：复杂的细胞网络是哺乳动物中枢神经系统（CMS）功能基础的基础。 了解这些网络的生理动力学，换句话说，了解相互作用的细胞群之间的信号传导如何产生和调节有意义的生理信息，将直接有助于我们了解CNS在健康状态下的功能以及在疾病中的功能。 目前，我们对神经元和神经胶质网络动力学的机械理解非常有限，即使我们理解了其基础的分子功能单元（即，突触）。 一种方法是应用网络理论来表征神经元和神经胶质网络。 网络理论是统计力学的一个分支，它独立于网络的物理细节对复杂网络进行分类，并提供对其动力学行为的理解。 将网络理论应用于神经元和神经胶质网络需要知道它们的结构或拓扑结构。 然而，高通量的计算密集型测量神经元和神经胶质细胞之间的分子信号传导，并提取定量信息的基础网络结构是不可能的，目前的技术。 因此，需要的是算法和软件，将允许生理神经元和神经胶质网络的高通量表征和分析。 在这里，我们建议开发计算工具，使我们能够映射功能神经元和神经胶质信号网络的空间和时间拓扑结构，并在网络理论的背景下对其进行分类和分析。 我们提出了这样做所需的算法和编程的详细讨论，并说明了这样一个程序的测试版的操作和验证。 我们建议，使用这种方法，神经元和神经胶质网络可以在已知的数学网络类型中进行分类，并根据它们被分类的网络类型的定量属性来表现。 我们还提出了初步的实验数据，首次表明，钙信号在星形胶质细胞网络，映射使用我们的软件工具，有一个以前未识别的拓扑结构。 我们建议，健康的神经元和神经胶质细胞的网络拓扑结构重塑损伤后，神经病理疾病状态的诱导和维护的基础，使这些研究结果的临床意义和调查他们所需的计算工具的发展非常重要。

DESCRIPTION (provided by applicant): Complex cellular networks underlie the functional foundation of the mammalian central nervous system (CMS). Understanding the physiological dynamics of these networks, in other words understanding how signaling between interacting groups of cells produce and modulate meaningful physiological information, will directly contribute to our understanding of how the CNS functions in health and how it fails in disease. At present, our mechanistic understanding of the dynamics of neuronal and glial networks is very limited, even though we understand the molecular functional unit that underlies it (i.e., the synapse). One approach is to apply network theory to characterize neuronal and glial networks. Network theory is a branch of statistical mechanics that classifies complex networks independent of the physical details of the network and provides an understanding of its dynamical behavior. Applying network theory to neuronal and glial networks requires knowing their structure or topology. However, high throughput computationally intensive measurements of molecular signaling between neurons and glia, and the extraction of quantitative information about their underlying network structure is not possible given current techniques. What is needed therefore, are algorithms and software that will allow the high throughput characterization and analysis of physiological neuronal and glial networks. Here, we propose to develop computational tools that will allow us to map the spatial and temporal topology of functional neuronal and glial signaling networks, and classify and analyze them within the context of network theory. We present a detailed discussion on the algorithms and programming required to do so, and illustrate the operation and validation of a beta version of such a program. We propose that using this approach, neuronal and glial networks can be classified within known mathematical network types and behave as dictated by the quantitative properties of the network types they are classified into. We also present preliminary experimental data showing for the first time that calcium signaling in astrocyte networks, mapped using our software tools, have a previously unidentified topology. We propose that the network topologies of healthy neurons and glia remodel following injury and underlie the induction and maintenance of neuropathological disease states, making the clinical significance of these findings and the development of the computational tools required to investigate them very important.