The factors influencing the occurrence and development of diseases are often complex and intricate. If we view each factor as a "node" and the connections between them as "edges" from a network perspective, then humans may be able to explore the underlying mechanisms of disease formation from a new perspective. On October 21st, a reporter from Science and Technology Daily learned that Professor Wu Rongling and his statistical team led by the Beijing Yanqi Lake Applied Mathematics Research Institute, as well as Wu Shuang, a doctoral student at Beijing Forestry University, have innovatively used mathematical methods to construct the statistical physics network model idopNetworks. Using the GLMY homology theory developed by scientist Qiu Chengtong and his collaborators, they analyze metabolic network models of different diseases, Explore the impact of various factors and their interactions on human diseases. The research paper, titled "Metabolic Physics of Complex Diseases," was recently published in the Proceedings of the National Academy of Sciences, providing new ideas for analyzing the causes of complex diseases, guiding the treatment of complex diseases, and designing related drugs. Unlike existing low dimensional network models, the statistical physical network model constructed by the team has achieved two major innovations. Firstly, the team constructed a comprehensive and dynamic network model, viewing disease as a complex network system composed of many factors (such as metabolic substances). By introducing the principles of evolutionary game theory, the role of each factor in the system was decomposed into two components, including the role of the factor itself, namely independent effects, and the influence of coexisting factors on it, namely dependent effects. From this, the contribution of each factor to the system can be clearly reflected. Subsequently, the team constructed a comprehensive and personalized network using independent effects as "nodes" and dependent effects as "edges", and referred to it as idopNetworks. The second innovation lies in the introduction of homology theory in algebraic topology to analyze networks. The team utilizes GLMY homology theory and integrates mathematical theories such as directed graph theory to analyze the roadmap of signal propagation from one factor to another in the network, analyzing the topological laws of system state changes, and tracking the topological structure changes of the network, in order to better understand the mechanism of disease occurrence and development. Inflammatory bowel disease is an idiopathic intestinal inflammatory disease, and its etiology and pathogenesis are not yet fully understood. This study takes inflammatory bowel disease as a case study and utilizes existing clinical data to construct a metabolic interaction network idopNetworks related to inflammatory bowel disease, obtaining the interaction relationships of different metabolites. Traditional methods can only identify individual metabolites significantly associated with inflammatory bowel disease, while idopNetworks found that the effects of these individual metabolites do not come from their independent effects, but from the regulation of other metabolites, i.e. dependent effects. Changing the regulatory relationship between metabolites can lead to changes in their own actions. IdopNetworks also revealed changes in metabolite interactions when patients transition from a healthy state to inflammatory bowel disease and from inflammatory bowel disease to a healthy state. Inflammatory bowel disease includes two types: ulcerative colitis and Crohn's disease, and the similarities and differences in their metabolic mechanisms have not been systematically studied. The team uses GLMY homology theory to analyze the ido of the two