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Collaborative Research: Analysis of the Mammalian Olfactory Code

Collaborative Research: Analysis of the Mammalian Olfactory Code
合作研究:哺乳动物嗅觉密码分析
批准号:
1556207
负责人:
Hiroaki Matsunami
金额:
$127.08万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-11-01 至 2020-10-31

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中文摘要
翻译
该项目由美国国家科学基金会“破解嗅觉密码”创意实验室开发,由化学部门的生命过程化学项目、数学科学部门的数学生物学项目、物理部门的生命系统物理学项目、综合有机系统部门的神经系统集群、生物基础设施部门和新兴前沿部门共同资助。哺乳动物的嗅觉可以说是动物王国中最复杂的感觉系统。在复杂的环境中,数以百计的嗅觉感受器被用来探测大量具有敏锐灵敏度的化学物质。该合作项目结合了生物化学、神经生物学、基因组学、数学和新技术,以了解哺乳动物嗅觉系统如何检测、编码并从化学刺激中提取意义。该项目的目标是:(1)阐明化学感觉如何转变为嗅觉感知的基本神经机制;(2)为嗅觉科学家提供大量的科学资源;(3)为更广泛的受众提供有价值的信息,包括分子进化、化学生态学、风味和香味群落;(4)建立研究生物系统的新技术和数学框架;(5)促进化学传感技术在环境监测、食品安全和国土安全方面的应用。该项目还提供从高中到博士后的培训机会,并通过与当地科学博物馆、科学学习中心及其媒体的合作提供教育机会和推广。这个项目的努力是围绕三个目标组织的,重点是关于气味身份和气味价(吸引力/厌恶)的信息是如何在嗅觉受体水平上编码的(目的1);在嗅球内,气味信息首先被处理(目标2);以及杏仁核皮层,在那里气味编码可能与其他信息流整合(目的3)。该项目的完成需要开发和使用广泛的创新方法,包括将所有人类和小鼠的气味受体映射到它们所结合的化学物质上,使用行为分析来定义这些化学物质的固有价,将所有气味受体投影映射到嗅球上,从功能上表征它们在嗅球和皮质杏仁核中的神经表征,并利用新颖的数学方法,在感觉神经元、嗅球肾小球和杏仁核的水平上,理解气味编码和嗅觉神经回路的潜在结构。每个目标的实现都需要具有不同专业知识的团队成员之间的密切合作,包括分子生物学、行为神经科学、体内功能成像以及复杂数据集的数学和理论分析。这里实施的多学科策略有望导致对哺乳动物如何感知和理解其化学环境的综合和全面的理解。
英文摘要
This project was developed during a NSF Ideas Lab on "Cracking the Olfactory Code" and is jointly funded by the Chemistry of Life Processes program in the Chemistry Division, the Mathematical Biology program in the Division of Mathematical Sciences, the Physics of Living Systems program in the Physics Division, the Neural Systems Cluster in the Division of Integrative Organismal Systems, the Division of Biological Infrastructure, and the Division of Emerging Frontiers.The mammalian sense of smell is arguably the most complex sensory system in the animal kingdom. Hundreds of olfactory receptors are deployed to detect a vast array of chemicals with exquisite sensitivity in complex environments. This collaborative project combines biochemistry, neurobiology, genomics, mathematics and new technologies to understand how the mammalian olfactory system detects, encodes and extracts meaning from chemical stimuli. The goals of this project are to: (1) elucidate fundamental neural mechanisms for how chemical sensation turns into the perception of a smell; (2) produce a vast array of scientific resources to olfactory scientists; (3) provide valuable information for broader audiences, including for molecular evolution, chemical ecology, and flavor and fragrance communities; (4) establish new technologies and mathematical frameworks to study biological systems; and (5) facilitate applied chemical sensing technologies for environmental monitoring, food safety, and homeland security. The project also offers training opportunities from the high school to the postdoctoral trainee level, and educational opportunities and outreach through partnerships with local science museums as well as science learning centers and their media outlets.This project's efforts are organized around three aims that focus on how information about odor identity and odor valence (attractiveness/aversiveness) is encoded at the level of olfactory receptors (Aim 1); within the olfactory bulb, where odor information is first processed (Aim 2); and the cortical amygdala, where odor codes may integrate with other information streams (Aim 3). Completion of the project entails the development and use a broad array of innovative approaches that include mapping all human and mouse odorant receptors to the chemicals they bind, defining the innate valence of these chemicals using behavioral assays, mapping all odorant receptor projections to the olfactory bulb, functionally characterizing their neural representations in the olfactory bulb and cortical amygdala, and using novel mathematical approaches to understand the underlying structure of odor coding and olfactory neural circuits at the level of sensory neurons, olfactory bulb glomeruli, and amygdala. Progress towards each aim involves close collaborations between team members with diverse expertise, including molecular biology, behavioral neuroscience, in vivo functional imaging, and mathematical and theoretical analysis of complex datasets. The multidisciplinary strategy implemented here promises to lead to an integrated and comprehensive understanding of how mammals sense and make sense of their chemical environments.
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