Unified Modeling And Analysis Of Biomolecular Networks Based On Extended Petri Nets

Taking bioinformatics as the research background,considering the difference between the acquisition speed of biological data and life science knowledge,and various types of gene regulation,signal transduction and metabolic networks formed by biological data types due to the study of specific biological functions,It is not possible to systematically interpret the dynamic process of biological changes,so this paper proposes a method of biomolecular network integration.The article is extended based on the standard Petri net.By improving the relevant semantics and the input and output relationships on the place and transition,a general model framework suitable for describing biomolecular networks is obtained,and appropriate network analysis methods(dynamic properties,structural properties)are selected for Stability,drug target analysis,explain the drug resistance phenomenon during treatment and provide theoretical strategies for clinical multi-target com bination therapy.And because the Petri net model is based on the characteristics of asynchronous network changes,it is closer to the real and complex biological environment than synchronous changes,which can better help to recognize the false cycle caused by synchronous Boolean network modeling and obtain a more detailed steady state.Possible results,laying the foundation for better identification of key targets.The main research contents of this article are divided into the following points:1.Using the standard Petri net as the base network,by extending the relevant semantics and the input and output relationship expressions on the place and the transition,a general model framework for describing biological systems is defined,and the transition,regulation,and reaction processes are remapped Correspondence between them,and the correspondence between the place and the molecular state,gives the input and output arc weight function corresponding to different adjustment methods.2.Modeling the metabolic network,taking cyclic metabolism as an example,the dynamic analysis of the cyclic process is carried out from the behavioral and structural characteristics of the system in turn,revealing its dynamic trajectory under the given initial state identification The changes in the amount of each molecule before and after the reaction were analyzed,and the characteristics of equilibrium stability and mass balance in the cycle were explained based on the invariant theory.3.Modeling the gene regulatory network.Through simulation experiments,a steady state(deadlock state)that may exist in the network was discovered from the perspective of state accessibility,and compared with the results in the literature(Boolean attractor).,Not only confirms the accuracy of the model architecture,but also provides a new and effective method for the stability analysis after the network has added interference.4.The probabilistic model detection idea is used to simplify the network structure,and a hepatocellular carcinoma signal transduction network is constructed.Using the network structure analysis method,without determining the initial identification,only by solving the invalid identification based on the transition trigger condition The siphon phenomenon existing in the Internet and the network,to determine whether the cell may be cancerous,and to identify the key path of cell cancer,to find the key factors affecting the normal growth and differentiation of cells,to provide predictions of key targets for clinical diagnosis and treatment,and to combine The concept can explain the multi-target combination therapy strategy of anti-resistance,and thus point out the main control points for preventing the occurrence and development of cancer.