Development and Analysis of Neuronal Network Models of Respiratory Rhythms

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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.