Network-based predictions of in vivo cardiac hypertrophy

Abstract

Cardiac hypertrophy is a common response of cardiac myocytes to stress and a predictor of heart failure. While in vitro cell culture studies have identified numerous molecular mechanisms driving hypertrophy, it is unclear to what extent these mechanisms can be integrated into a consistent framework predictive of in vivo phenotypes. To address this question, we investigate the degree to which an in vitro-based, manually curated computational model of the hypertrophy signaling network is able to predict in vivo hypertrophy of 52 cardiac-specific transgenic mice. After minor revisions motivated by in vivo literature, the model concordantly predicts the qualitative responses of 78% of output species and 69% of signaling intermediates within the network model. Analysis of four double-transgenic mouse models reveals that the computational model robustly predicts hypertrophic responses in mice subjected to multiple, simultaneous perturbations. Thus the model provides a framework with which to mechanistically integrate data from multiple laboratories and experimental systems to predict molecular regulation of cardiac hypertrophy.

Keywords: Cardiac hypertrophy; Computational modeling; Heart failure; Systems biology; Transgenic mice.