Modeling disease progression using dynamics of pathway connectivity

Abstract

Motivation: Disease progression is driven by dynamic changes in both the activity and connectivity of molecular pathways. Understanding these dynamic events is critical for disease prognosis and effective treatment. Compared with activity dynamics, connectivity dynamics is poorly explored.

Results: We describe the M-module algorithm to identify gene modules with common members but varied connectivity across multiple gene co-expression networks (aka M-modules). We introduce a novel metric to capture the connectivity dynamics of an entire M-module. We find that M-modules with dynamic connectivity have distinct topological and biochemical properties compared with static M-modules and hub genes. We demonstrate that incorporation of module connectivity dynamics significantly improves disease stage prediction. We identify different sets of M-modules that are important for specific disease stage transitions and offer new insights into the molecular events underlying disease progression. Besides modeling disease progression, the algorithm and metric introduced here are broadly applicable to modeling dynamics of molecular pathways.

Availability and implementation: M-module is implemented in R. The source code is freely available at http://www.healthcare.uiowa.edu/labs/tan/M-module.zip.