The research on molecular network models of complex disease

Abstract

Complex diseases such as cardiovascular disease, cancer and diabetes are major killers of human health. Unlike single gene defect diseases, complex diseases are usually believed to be associated with the interactions among genes and the interactions among genes and environmental factors. The underlying complex pathogeneses make both the early diagnosis and treatment difficult. Therefore the research of complex diseases is one of the major challenges of biomedical research in this century. Recently, the rapid accumulation of biological knowledge and multi-level high-throughput omics data, revolutionarily changed the research paradigm on complex disease. Instead of only focusing on single molecular, researchers are gradually extending their research to systematically analyzing genome-wide biomolecular interactions, i.e., biomolecular networks. In this context, bio-molecular network, which is a powerful tool to study complex diseases, enables systematically integration of high throughput biological data and plenty of biological knowledge. Recently, a large number of biological networks have been constructed, including protein interaction network, transcriptional regulation network, signal transduction network, metabolic network and so on. These studies have played a significant role in revealing the pathogeneses of complex diseases and promoting treatment or prevention of diseases. However, the biomolecular network research on complex diseases is still in its infancy. Due to the complexity of mechanisms and disease-related biological data, many challenges remain to be further explored and resolved. In this thesis, we use mathematical models of biomolecular networks to address some main challenges in the research of complex diseases. Specifically, we study molecular networks step by step from static, node dynamic, edge dynamic, to module dynamic of network construction and network analysis, and build several mathematical models to deepen the complex disease study by biomolecular networks concept.