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This article is part of the supplement: Selected articles from the Second Annual Translational Bioinformatics Conference (TBC 2012)

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Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers

Xiaoping Liu1, Rui Liu23, Xing-Ming Zhao4 and Luonan Chen13*

Author Affiliations

1 Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

2 Department of Mathematics, South China University of Technology, Guangzhou 510640, China

3 Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan

4 Department of Computer Science, School of Electronics and Information Engineering, Tongji University, China

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BMC Medical Genomics 2013, 6(Suppl 2):S8  doi:10.1186/1755-8794-6-S2-S8

Published: 7 May 2013



Type 1 diabetes (T1D) is a complex disease and harmful to human health, and most of the existing biomarkers are mainly to measure the disease phenotype after the disease onset (or drastic deterioration). Until now, there is no effective biomarker which can predict the upcoming disease (or pre-disease state) before disease onset or disease deterioration. Further, the detail molecular mechanism for such deterioration of the disease, e.g., driver genes or causal network of the disease, is still unclear.


In this study, we detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T1D.


Two dynamical network biomarkers were obtained to signal the emergence of two critical deteriorations for the disease, and could be used to predict the upcoming sudden changes during the disease progression. We found that the two critical transitions led to peri-insulitis and hyperglycemia in NOD mices, which are consistent with other independent experimental results from literature.


The identified dynamical network biomarkers can be used to detect the early-warning signals of T1D and predict upcoming disease onset before the drastic deterioration. In addition, we also demonstrated that the leading biomolecular networks are causally related to the initiation and progression of T1D, and provided the biological insight into the molecular mechanism of T1D. Experimental data from literature and functional analysis on DNBs validated the computational results.