High Throughput Mapping of Neuronal and Glial Networks

复杂的细胞网络是哺乳动物中枢神经系统的功能基础 (CMS)。了解这些网络的生理动态，换句话说，了解如何 相互作用的细胞组之间的信号产生和调节有意义的生理信息， 将直接有助于我们理解CMS在健康中如何发挥作用，以及它在疾病中如何失败。在… 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，甚至 虽然我们了解它背后的分子功能单位(即突触)。一种方法是 应用网络理论来描述神经元和神经胶质网络。网络理论是统计学的一个分支 独立于网络的物理细节对复杂网络进行分类的机制，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。然而，高吞吐量的计算密集型测量 神经元和胶质细胞之间的分子信号转导及其定量信息的提取 根据当前的技术，底层网络结构是不可能的。因此，我们需要的是 算法和软件将允许高通量表征和分析生理学 神经元和神经胶质网络。在这里，我们建议开发计算工具，使我们能够绘制 功能神经元和神经胶质信号网络的空间和时间拓扑，并进行分类和分析 它们是在网络理论的背景下进行的。我们给出了详细的讨论算法和 执行此操作所需的编程，并说明此类测试版本的操作和验证 程序。我们建议使用这种方法，神经元网络和神经胶质网络可以被归类为已知的 数学网络类型和行为由它们的网络类型的数量属性决定 被归类为。我们还提供了初步的实验数据，首次表明钙 用我们的软件工具绘制的星形胶质细胞网络中的信号，有一个以前未被识别的拓扑学。 我们认为，正常神经元和胶质细胞的网络拓扑结构在损伤后重塑，并奠定了 神经病理疾病状态的诱导和维持，使得这些疾病的临床意义 调查结果和所需计算工具的开发非常重要。

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 CMS 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 classifiescomplex 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 measurementsof 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 softwarethat 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 networktypes 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 toplogy. We propose that the networktopologies 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.