# Multi-scale systems analysis of blood pressure control and hypertension

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $503,995

## Abstract

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.

## Key facts

- **NIH application ID:** 9879768
- **Project number:** 5R01HL139813-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** DANIEL A BEARD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $503,995
- **Award type:** 5
- **Project period:** 2018-06-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9879768

## Citation

> US National Institutes of Health, RePORTER application 9879768, Multi-scale systems analysis of blood pressure control and hypertension (5R01HL139813-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9879768. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*