Development and Analysis of Neuronal Network Models of Respiratory Rhythms

实验揭示了哺乳动物脑干中存在一种呼吸神经控制系统。有证据表明，该系统维持稳定的呼吸节律，有助于呼吸适应代谢需求的快速变化以及更渐进的过程，如衰老和疾病。然而，这一系统运作的机制仍在加紧调查之中。本项目将重点关注与呼吸神经控制有关的核心问题，分析赋予特定呼吸神经元起搏器功能的机制，以及在完整的哺乳动物呼吸网络中，不同神经元群相互作用产生呼吸节律的方式。拟议的研究将导致新的结果和实验预测，关于特定的网络特征，包括神经元的内在特性和它们之间相互作用的特征，如何促进产生的节奏的产生和调节。特别是，结果将表明，内部反馈系统可能针对调节呼吸以响应不断变化的条件的关键网络元素，并且可能在病理状态下受到损害。这些进步将通过开发和数学分析神经网络模型来实现，这些模型具有不同程度的生物细节，受实验数据的限制。这项工作的重要部分将通过与两个实验性神经科学实验室的合作进行。这些相互作用将提供对实验数据的直接访问，并将导致模型预测的实验测试。该项目将涉及研究生和本科生的贡献，他们将在参与的过程中获得宝贵的研究训练。通过将新颖的数学和生物学成果纳入多个系学生的研究生课程，在数学生物学工作组中展示新兴思想，监督神经科学学生的研究轮转，以及参与匹兹堡大学的跨学科中心，将实现更广泛的培训影响。

Experiments have revealed the existence of a neural control system for respiration within the mammalian brain stem. Evidence indicates that this system maintains a stable respiratory rhythm and contributes to the adaptability of respiration to rapid changes in metabolic demand as well as to more gradual processes, such as aging and disease. The mechanisms through which this system operates, however, remain under intense investigation. This project will focus on central issues relating to the neural control of respiration, providing an analysis of the mechanisms that confer pacemaker capabilities on particular respiratory neurons and the way in which different groups of neurons interact, within the full mammalian respiratory network, to produce respiratory rhythms. The proposed research will lead to new results and experimental predictions about how specific network features, including intrinsic properties of neurons and characteristics of the interactions between them, contribute to the generation, and the modulation, of the rhythms that arise. In particular, results will suggest key network elements that may be targeted by internal feedback systems to modulate respiration in response to changing conditions and that may be compromised in pathological states.These advances will be achieved through the development and mathematical analysis of neuronal network models featuring varying levels of biological detail, constrained by experimental data. Significant portions of this work will proceed through collaborations with two experimental neuroscience labs. These interactions will provide direct access to experimental data and will lead to experimental testing of model predictions. The project will involve the contributions of graduate and undergraduate students, who will gain valuable research training in the course of their participation. Broader training impacts will be achieved through the incorporation of novel mathematical and biological results into graduate courses taken by students across multiple departments, the presentation of emerging ideas within a Math Biology Working Group, the supervision of research rotations by neuroscience students, and participation in interdisciplinary centers at the University of Pittsburgh.