Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera

Abstract

Significance A major contributor to heart valve disease is the excessive buildup of scar-like tissue in the valve, which can hinder the ability of the valve to open and close and can ultimately lead to heart failure. Controlling this scar tissue remodeling is very difficult due, in part, to a complex cellular regulation system and, in part, to large variabilities between different patients. We have built a computational model of the cell biochemical network that regulates valve remodeling, which enables virtual predictions of valve scarring given patient-specific biochemical levels. With this model, we ran personalized drug screens to predict each patient’s response to particular therapies, and follow-up cell culture experiments validated our predictions with over 80% accuracy.