Tracing dynamic biological processes during phase transition

Individual-specific edge-network analysis for disease prediction

Predicting pre-disease state or tipping point just before irreversible deterioration of health is a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory opens a new way to study this problem by exploring rich dynamical and high-dimensional information of omics data. Although theoretically ENA has the ability to identify the pre-disease state during the disease progression, it requires multiple samples for such prediction on each individual, which are generally not available in clinical practice, thus limiting its applications in personalized medicine. In this work to overcome this problem, we propose the individual-specific ENA (iENA) with DNB to identify the pre-disease state of each individual in a single-sample manner. In particular, iENA can identify individual-specific biomarkers for the disease prediction, in addition to the traditional disease diagnosis. To demonstrate the effectiveness, iENA was applied to the analysis on omics data of H3N2 cohorts and successfully detected early-warning signals of the influenza infection for each individual both on the occurred time and event in an accurate manner, which actually achieves the AUC larger than 0.9. iENA not only found the new individual-specific biomarkers but also recovered the common biomarkers of influenza infection reported from previous works. In addition, iENA also detected the critical stages of multiple cancers with significant edge-biomarkers, which were further validated by survival analysis on both TCGA data and other independent data.

Autophagy mediates phase transitions from cell death to life

Autophagy is a lysosomal degradation pathway, which is critical for maintaining normal cellular functions. Despite considerable advances in defining the specific molecular mechanism governing the autophagy pathway during the last decades, we are still far from understanding the underlying principle of the autophagy machinery and its complex role in human disease. As an alternative attempt to reinvigorate the search for the principle of the autophagy pathway, we in this study make use of the computer-aided analysis, complementing current molecular-level studies of autophagy. Specifically, we propose a hypothesis that autophagy mediates cellular phase transitions and demonstrate that the autophagic phase transitions are essential to the maintenance of normal cellular functions and critical in the fate of a cell, i.e., cell death or survival. This study should provide valuable insight into how interactions of sub-cellular components such as genes and protein modules/complexes regulate autophagy and then impact on the dynamic behaviors of living cells as a whole, bridging the microscopic molecular-level studies and the macroscopic cellular-level and physiological approaches.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.

Network biomarkers reveal dysfunctional gene regulations during disease progression

Extensive studies have been conducted on gene biomarkers by exploring the increasingly accumulated gene expression and sequence data generated from high-throughput technology. Here, we briefly report on the state-of-the-art research and application of biomarkers from single genes (i.e. gene biomarkers) to gene sets (i.e. group or set biomarkers), gene networks (i.e. network biomarkers) and dynamical gene networks (i.e. dynamical network biomarkers). In particular, differential and dynamical network biomarkers are used as representative examples to demonstrate their effectiveness in both detecting early signals for complex diseases and revealing essential mechanisms on disease initiation and progression at a network level.