A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

ABSTRACT: Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system

SUBMITTER: Anderson WD 

PROVIDER: S-EPMC5521738 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature