CRCNS: Activity-dependent growth cone guidance

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DESCRIPTION (provided by applicant): Navigation of growth cones to their targets is essential for the establishment of neural circuits during nervous system development. Growth cones, at the tips of growing neurites, navigate through a variety of extracellular environments in which they must sense, integrate and respond to a myriad of signals. This project seeks to investigate the molecular mechanisms of integration of the multi-signaling pathways required for growth cone guidance during normal nervous system development. The complexity of the interactions of these multi-signaling pathways during growth cone guidance, such as coincident detection and cross-talk signaling, as well as their spatiotemporal changes during development, prevents their elucidation by biological methods alone. The goal of this project is to bring together biologists and computational scientists to determine the mechanisms by which neuronal activities evoked by multiple neurotransmitter-guidance signals are transduced before synaptic contacts are established, using well established experimental methods and advanced computational analyses. We aim to experimentally investigate the developmental changes in intrinsic growth cone properties that determine growth cone responses to external signals, i.e., ion channels and neurotransmitter signals, and to encode them in a computational model that can simulate the normal growth cone behavior that occurs in response to external guidance signals during nervous system development in vivo. We will determine the effects of developmental stage-dependent changes in neurotransmitter and guidance signals on growth cone turning in vitro, and subsequently test their dependencies both in vitro and in vivo, and develop computational, multi-signal integration and chemo-sensing growth cone models. Multi-signal integration models will be used to simulate biological responses of growth cones, to predict the most biologically efficient bi-directional guidance signaling mechanisms and to predict whether there is sufficient data to allow a faithful representation of growth cone multi-signal integration to fully describe normal growth cone migration as it occurs in vivo. Chemo-sensing models will be used to decode the external environmental chemical gradients that growth cones encounter during their guidance in vivo, and predict the essential environmental parameters required for growth cone behavior to allow their confirmation by direct experimentation.