PostDoctoral Research Fellowship in Biological Informatics FY 2006

该项目是在2006年生物信息学博士后研究奖学金计划下获得的。这项跨学科的研究将现代计算技术与现有的实验数据相结合，以研究稳定的双足跑步的控制策略。该研究涉及创建一个鸡大小的地面鸟，珍珠鸡（Numida musculagris）的简化计算机模型，其中包括神经系统，肌肉骨骼系统及其相互作用的元素。珍珠鸡是一个理想的模型系统，因为它是一个有能力的跑步和广泛的研究动物在实验运动力学和神经肌肉生理学。大量的实验数据可用于验证模型和测试特定的控制假设。这项研究填补了我们对身体力学和神经控制如何相互作用以在崎岖地形上创造稳定和敏捷运动的理解的重要空白。这项博士后奖学金提供了学习计算工具的机会，补充了研究员在实验生理学和生物力学方面的专业知识。在密歇根大学的丹尼尔费里斯实验室的培训将包括计算机模拟，非线性动态建模和控制理论的技术。该研究员与工程，数学和计算机科学的研究人员互动，这些研究人员可能会发展成为长期的跨学科合作。此外，该奖学金提供了一个机会，开发一种新的研究工具，促进神经控制和生物力学之间的接口的新发现。该模型还为生物力学，运动学，生理学和神经科学的本科生和研究生提供了新的教育资源。

This project is awarded under the Postdoctoral Research Fellowships in Biological Informatics Program for 2006. This interdisciplinary research combines modern computational techniques with existing experimental data to investigate control strategies for stable bipedal running. The research involves creation of a simplified computer model of a chicken-sized ground bird, the guinea fowl (Numida meleagris), that includes elements of the nervous system, the musculo-skeletal system and their interaction. The guinea fowl is an ideal model system for this research because it is a capable runner and extensively studied animal in experimental locomotor mechanics and neuromuscular physiology. A large body of experimental data is available to validate the model and test specific control hypotheses. The research bridges an important gap in our understanding of how body mechanics and neural control interact to create stable and agile movement over rough terrain.This postdoctoral fellowship provides an opportunity to learn computational tools that complement the Fellow's expertise in experimental physiology and biomechanics. Training in the lab of Daniel Ferris at the University of Michigan will include techniques in computer simulation, nonlinear dynamic modeling, and control theory. The Fellow interacts with researchers in engineering, mathematics and computer science that are likely to develop into long-term interdisciplinary collaborations. Additionally, the fellowship provides an opportunity to develop a new research tool that facilitates new discovery at the interface between neural control and biomechanics. The model also provides a new educational resource for undergraduate and graduate students in biomechanics, kinesiology, physiology, and neuroscience.