Shin Ishii
Keynote lecture
Will talk about: Modeling and simulations of neuronal morphology
Shin Ishii received his ME and PhD degrees in 1988 and 1997, respectively, from the University of Tokyo. Before joining Kyoto University in July 2007, he has been a professor at Nara Institute of Science and Technology after a 9-year career at two private research laboratories.
His major research area is computational biology including reinforcement learning, systems neurobiology, bioinformatics and machine learning.
Neuronal morphology would be an information processing basis of neurons. During development, neurons acquire their morphology to work as information processing elements. Even in a uniform environment, typical neurons produce single axons from multiple neurites in a stable manner. First, we introduce a computational model of this symmetry-breaking phenomenon, neuronal polarization, which is characterized by stability and flexibility. After establishing the single-axon structure, the axon is guided by extra-cellular guidance molecules, in order to perform appropriate neuronal wiring. However, this axon guidance is an extremely complicated phenomenon; it shows bidirectionality, attraction and repulsion, depending largely on the status of intracellular calcium signaling. To know the mechanism underlying this calcium-dependent bidirectionality, we next introduce a computational model of intracellular biophysics in the axon guidance. During the guidance, actin filaments in a neuronal growth cone are dynamically regulated by extracellular guidance molecules, which produce driving force of the axon; this dynamic cytoskeleton is also involved in cellular chemotaxis. Then, we introduce a multi-physical model of cellular chemotaxis and show its large-scale simulations. Our simulation model includes actin filament dynamics, membrane, and intracellular signaling related to cytoskeleton regulation. Through simulations of cellular chemotaxis and invasion, we observed that the cell dynamically changes its morphology to appropriately adapt to the surrounding environment. In addition, we will introduce some imaging techniques to examine the morphology of neurons and neural networks.
