Development and Analysis of Neuronal Network Models of Respiratory Rhythms

实验揭示了哺乳动物脑干内存在呼吸的神经控制系统。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，这一系统的运行机制仍在密切调查中。这个项目将集中在与呼吸的神经控制有关的中心问题上，提供对赋予特定呼吸神经元起搏能力的机制的分析，以及在整个哺乳动物呼吸网络中不同神经元群体相互作用以产生呼吸节律的方式。这项拟议的研究将导致新的结果和实验预测，即特定的网络特征，包括神经元的内在属性和它们之间相互作用的特征，如何有助于产生和调制出现的节律。特别是，结果将提出内部反馈系统可能针对的关键网络元件，以调节呼吸以应对不断变化的条件，以及在病理状态下可能受到损害。这些进展将通过开发和数学分析具有不同水平的生物细节的神经网络模型来实现，受实验数据的限制。这项工作的很大一部分将通过与两个实验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，并将导致对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，他们将在参与过程中获得宝贵的研究培训。将通过将新的数学和生物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介绍新出现的想法、监督神经科学学生的研究轮换以及参与匹兹堡大学的跨学科中心来实现更广泛的培训效果。

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.