Identifying Temporal Biomarkers of Disease Development through a Thermodynamics-enriched Ensemble Framework

Disease development is viewed as a highly dynamic process, where tipping points happen in many complex diseases with irreversible sudden deteriorations in patients’ medical situations. Developing effective strategies for identifying temporal biomarkers, such as identifying temporal protein interaction networks and detecting tipping points, is significantly meaningful for designing an appropriate intervention to prevent the deteriorations. Existing studies inferred dynamical networks from gene expression profiles, uniquely based on static protein-protein interactions, and identified tipping points by a single outlier analysis of dynamic networks under certain assumptions.In this paper, we proposed to identify tipping points of dynamical protein-protein interaction networks through a thermodynamics-enriched ensemble framework In this framework, we constructed dynamical protein-protein interaction networks (DPPINs) by integrating tissue-based direct protein-protein interactions with multiple temporal or non-temporal sources. Through a thermodynamic analysis of these dynamic networks, we identified an abrupt change of free energy at a tipping point and validated this result by relative entropy of dynamic networks.