Systems biology for complex diseases

Systems biology views a living organism as an interacting and dynamical network of genes, proteins, and biochemical reactions which give rise to the function and behavior of that organism, and is considered as a powerfully analytical approach to reveal the essential mechanisms of initiation and progression for complex diseases such as cancer, cardiovascular disease, and diabetes. The classical bio-medicine based on molecular biology, cell biology, genetics, and other experimental biology has made significant progress against diseases in general. However, the researchers on the bio-medicine area still face the great challenge against the complex diseases, since the methodology of the classical experimental biology is mainly based on studying individual genes and proteins and treating organisms as simple and linear systems, which are not good enough to solve increasingly complicated problems of the diseases. Therefore, the methodologies and techniques of systems biology are strongly demanded for analyzing the molecular mechanisms of the complex diseases and providing new solutions to fight the complex diseases. The aim of this special issue is to collect the latest advances in developing new computational and experimental approaches to decipher the complexity of diseases or on providing the implications and explanations for disease mechanisms and therapeutic endeavors in drug research, from systems biology perspective. In this issue, there are two review papers, three research articles and two letters, which present recent research efforts in various aspects for understanding mechanisms of the complex diseases, including prostate cancer, ovarian cancer, liver cancer, diabetes, and neuronal diseases. In the first review paper, Hirata et al. provided the details on recent development of quantitative mathematical models for prostate specific antigen dynamics of prostate cancer patients, and demonstrated that an intermittent therapy (intermittent androgen suppression) designed based on a quantitative mathematical model can be used to cope with biochemical relapse. The authors also showed that dynamical information could be used to identify those patients who might benefit from the intermittent therapy versus those who might not. In contrast to the insight from mathematical models, Kuwabara and Asashima reviewed recent findings regarding the use of adult olfactory bulb-derived neural stem cells (OB NSCs) as a potential tool for diabetes therapy based on the experiment evidences, and further discussed the potential of OB NSC-based pharmaceutical applications for neuronal diseases and mental disorders. Besides the review papers, this issue reported several timely developments in understanding molecular networks of complex diseases. He et al. developed a novel framework to identify distinct patterns of gene coexpression networks and inflammation-related modules from genome-scale microarray data upon viral infection, and further classified them into oncogenic and dysfunctional ones. In this study, the authors revealed two types of driving modules related to infection for carcinogenesis in hepatitis B virus and hepatitis C virus, respectively. These findings provide novel insights into viral hepatocarcinogenesis and disease progression, and also demonstrate the advantages of an integrative and comparative network analysis over existing differential expression-based approach and virus–host interactome-based approach. In another research paper, Won et al. employed a systems approach by combining biochemical experiments with in silico simulations to investigate the resistance mechanism and functional consequences of MEK inhibitor. Based on an integrative model of ERK and PI3K signaling pathways by considering the cross-talk between Ras and PI3K, the authors revealed that the cross-regulation between ERK and PI3K signaling pathways determined the tumoricidal efficacy of MEK inhibitor and further suggested promising candidates for combination therapy. As another topic to study the drug resistance, Choi et al. investigated whether AIMP2-DX2 involved in the pro-apoptotic role of AIMP2-Fin TNF-signaling pathway, and whether it could be an effective target that can overcome the chemoresistance of ovarian cancer, and further suggested that splicing variant of AIMP2 was an effective target against chemoresistant ovarian cancer. In the first letter, Cui et al. addressed a fundamental question for the cancer research: what drives cancer to grow in an accelerated fashion as it evolves. Various proposals have been made so far regarding the possible drivers of cancer growth such as driver mutations and autonomous growth signaling, but this question has not yet been satisfactorily solved. To answer this question, the authors developed a new model for explaining what possibly derives cancer growth/accelerated growth and how based on the analysis of genome-scale transcriptomic data on seven solid cancers, which suggested that hypoxia and the need for survival drive cancer to grow increasingly faster. In another letter, Yuan et al. present a new strategy to analyze time serial prostate cancer gene expression data by introducing a new concept of the time conservation of gene expression, and revealed several potential disease genes of the prostate cancer with high time conservative scores, which were further validated by various biological evidences. A complex disease is generally resulted from complicated interplay of many biological processes and dysfunctional systems, which are involved in the interactions among various genetic factors, epigenetic factors, and environmental factors. Clearly, the system-level study is required to reveal the fundamental principle of disease initiation and progression. Although systems biology approach to elucidate the essential mechanisms of human disease is challenging, collaborative efforts are facilitating progress towards this ultimate goal, as reported in this issue. We would like to thank all of the authors for their high-quality works contributed to this special issue and all of the anonymous reviewers for their great efforts and expert comments in evaluating the manuscripts.