Multi-scale systems analysis of blood pressure control and hypertension

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Abstract This proposed project investigates the mechanisms underlying 'essential' (or 'primary') hypertension by undertaking a systems investigation of pathway interactions in cardiovascular phenotypes (in animal models and patients). Hypertension and hypertensive disease are complex whole body syndromes that are unlikely to be effectively understood in terms of single-cause/single-effect relationships. We propose that one of the central barriers to understanding these systems interactions has been an inability to formulate system-level hypotheses in ways that yield experimentally testable predictions. Using systems approaches to analyzing and interpreting molecular, cellular, tissue, organ, and organ-system data our research team has begun to make transformational progress on understanding the multifactorial nature of cardiovascular phenotype and cardiovascular disease. We have developed multi-scale models of components of the cardiovascular system responsible for processes ranging from beat-to-beat cardiovascular dynamics to long-term regulation of blood volume. Using these models, we have identified and tested new hypotheses on the relationship between vascular mechanics, autonomic function, renal function, arterial pressure, and the derangement of mechanical- energetic coupling in hypertensive disease. In the proposed project we will build on these studies to probe and identify the mechanisms underlying the complex cardiovascular phenotypes of the spontaneously hypertensive and Dahl salt sensitive rat models of hypertension. In Aims 1 and 2 a panel of phenotyping protocols will be applied to probe cardiovascular function in these two complimentary rodent models (and relevant controls) during the development of hypertension. Data will from these studies will be analyzed with computational models to: (i.) determine which existing hypotheses are able to explain the observations in the experimental models and which may be ruled out; (ii.) determine what revisions/refinements to existing hypotheses are needed to potentially explain the data; (iii) identify potential inter-dependencies in existing and novel hypotheses; and finally (iv.) to design experiments to rule out competing hypotheses. Under Aim 3 we will apply what we learn from the animal studies to design new ways to better diagnose hypertension in the clinic, translating the basic discoveries and associated computer models for future applications in precision medicine and quantitative systems pharmacology.