title: Identifying critical transitions and their leading biomolecular networks in complex diseases.
abstract: Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.
authors.affiliation: Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo , Tokyo 153-8505, Japan.
authors.forename: Rui
authors.initials: R
authors.lastname: Liu
authors.forename: Meiyi
authors.initials: M
authors.lastname: Li
authors.forename: Zhi-Ping
authors.initials: ZP
authors.lastname: Liu
authors.forename: Jiarui
authors.initials: J
authors.lastname: Wu
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D000465:Algorithms; D006528:Carcinoma, Hepatocellular / Q000378:metabolism / Q000473:pathology; D056486:Chemical and Drug Induced Liver Injury / Q000378:metabolism / Q000473:pathology; D018450:Disease Progression; D019277:Entropy; D006801:Humans; D008113:Liver Neoplasms / Q000378:metabolism / Q000473:pathology; D008954:Models, Biological; D010705:Phosgene / Q000633:toxicity; D015398:Signal Transduction / Q000187:drug effects; D049490:Systems Biology
chemical_list: D010705:Phosgene