Learning Context-Specific Gene Regulatory Networks via In-Silico Conditioning

Cell adjusts its regulatory machinery in response to environmental changes to maintain its basic functionality. Different cellular conditions require different regulatory mechanisms that tightly regulate genes. Due to this tightly coordinated regulation, the expression of those genes should show consistent patterns within the same cellular contexts while such consistency disappears when corresponding context is disrupted. Based on previously developed statistics to identify genes whose expression pattern is significantly more consistent within a specific biologic context, we have developed an algorithm to identify novel cellular contexts and learn underlying context-specific gene regulatory networks.