High Throughput Mapping of Neuronal and Glial Networks

神经元和神经胶质网络的高通量绘图

• 批准号: 7588733
• 负责人: Gabriel A Silva
• 金额: $ 28.08万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7588733
• 关键词: Algorithms; Astrocytes; Behavior; Biological Neural Networks; Brain; Calcium Signaling; Cell Count; Cells; Central Nervous System Diseases; Communities; Complex; Computational algorithm; Computer software; Data; Development; Disease; Elements; Engineering; Environment; Figs - dietary; Foundations; Gliosis; Health; In Situ; In Vitro; Injury; Length; Maintenance; Maps; Measures; Molecular; Muller' s cell; Natural regeneration; Nerve Regeneration; Neuraxis; Neuroglia; Neuronal Plasticity; Neurons; Neurosciences; Operative Surgical Procedures; Physiological; Preparation; Process; Property; Pythons; Rattus; Retina; Retinal; Signal Transduction; Slice; Software Tools; Statistical Mechanics; Structure; Synapses; Techniques; Testing; Time; TimeLine; Validation; clinically significant; computerized tools; falls; graphical user interface; novel; novel strategies; programs; relating to nervous system; theories; user-friendly; web site

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.

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力学，换句话说， 相互作用的细胞群之间的信号传导产生并调节有意义的生理信息， 将直接有助于我们理解CMS在健康中的功能以及它在疾病中的功能。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 尽管我们了解它背后的分子功能单位（即突触）。一种方法是 应用网络理论来表征神经元和神经胶质网络。网络理论是统计学的一个分支 对复杂网络进行分类的机制，与网络的物理细节无关， 了解它的动力学行为。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，高通量计算密集型测量 神经元和神经胶质细胞之间的分子信号传导，以及关于它们的定量信息的提取。 在给定当前技术的情况下，底层网络结构是不可能的。因此，需要的是 算法和软件，将允许高通量表征和分析的生理 神经元和神经胶质网络。在这里，我们建议开发计算工具，使我们能够映射 功能性神经元和神经胶质信号网络的空间和时间拓扑结构，并分类和分析 在网络理论的背景下。我们提出了一个详细的讨论的算法， 编程需要这样做，并说明了这样的一个测试版的操作和验证 程序.我们建议使用这种方法，神经元和神经胶质网络可以被分类在已知的范围内。 数学网络类型和行为所规定的网络类型的定量属性，他们 被归类为。我们还提出了初步的实验数据表明，第一次，钙 使用我们的软件工具绘制的星形胶质细胞网络中的信号传导具有先前未识别的拓扑学。 我们认为，健康神经元和神经胶质细胞的网络拓扑结构在损伤后重塑，并成为损伤的基础。 诱导和维持神经病理疾病状态，使这些临床意义 调查结果和发展所需的计算工具来调查他们非常重要。