Mathematics of Neurite Outgrowth and Pathfinding

在这里提出的工作的总体目标是支持一个富有成效的跨学科合作，将同时产生有意义的定量研究结果在神经科学和促进收购的生理背景，这将使我能够开发一个独立的研究计划在定量建模神经元形态发生。 拟议的工作将在罗格斯大学合作神经科学中心进行，并将重点关注与背根神经节（DRG）神经元及其相关胶质细胞相关的发育和再生机制。 这项工作将涉及两个研究项目，将在Wise Young教授，医学博士和细胞生物学神经科学主席，以及Martin Grumet教授，博士和中心主任的指导下进行，与适当的教师，博士后研究员，研究生和中心附属的访问科学家密切合作。 这项工作的目的是定量测试的假设，背根神经节（DRG）神经突起在体外的形态可以很好地描述了一个随机模型，这种随机形态的功能是有效地探索空间搜索的目标。 将用于实现这些目标的方法将涉及轴突和神经胶质结构的直接数值模拟，这些结构经过验证和修改以符合神经元培养物的3D成像雅阁。 在整个工作中，指导目标将是使用建模来获得具体的，可测试的预测。该IGMS项目由MPS多学科活动办公室（OMA）和数学科学部（DMS）联合支持。

The overarching objective of the work proposed here is to support a productive interdisciplinary collaboration that will simultaneously generate meaningful quantitative research results in the neurosciences and foster the acquisition of the physiological background that will permit me to develop an independent research program in quantitative modeling of neuronal morphogenesis. The proposed work will be performed at the Center for Collaborative Neuroscience at Rutgers University, and will focus on developmental and regenerative mechanisms associated with Dorsal Root Ganglion (DRG) neurons and their associated glia. This work will involve two research projects to be performed under the guidance of Prof. Wise Young, MD PhD and Chair of Cell Biology & Neuroscience, and Prof. Martin Grumet, PhD and Director of the Center, in close collaboration with appropriate faculty, postdoctoral fellows, graduate students, and visiting scientists affiliated with the Center. The aims of this work are to quantitative test the hypotheses that morphogenesis of Dorsal Root Ganglion (DRG) neurites in vitro can be well described by a stochastic model, and that the function of such stochastic morphogenesis is to efficiently explore space in search of targets. The approach that will be used to achieve these aims will involve direct numerical simulations of axonal and glial structures that are validated and modified to accord with 3D imaging of neuronal cultures. Throughout this work, the guiding objective will be to use modeling to derive concrete, testable predictions.This IGMS project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).