Abstract
The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.
Highlights
Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis
We developed a model-based framework for the construction of dynamic regulatory networks using the integration of gene expression profiles with a prior knowledge of protein-protein interaction (PPI) networks
This study developed a new method to construct dynamic networks based on the combination of high-throughput gene expression data, a prior knowledge of network topology and ordinary differential equation (ODE)-based optimization
Summary
Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. One novel concept is the dynamical network biomarkers (DNBs) (i.e., a group of genes or proteins), which serve as a general early warning signal of a sudden deterioration before a critical transition occurs during disease initiation and progression. During this transition, the biological system is steered from a normal (or stable) state to a disease state. It is unrealistic to use these DNBs for clinical diagnoses
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