High Throughput Mapping of Neuronal and Glial Networks

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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.