With the development of experimental techniques such as yeast two-hybrid, mass spectrometry, chromosome immunoprecipitation, tandem affinity purification, protein chip, phage display and literature mining, a large number of molecular interaction data, also known as biological network data, such as protein interaction network, metabolic network, Gene expression network, gene regulatory network and signal transduction network have been generated. And these data showed an exponential growth trend.
Nowadays, a great deal of research work on biological network data has been carried out. Among them, one of important researches is the comparative analysis of biological network data, the alignment of biological networks. Through the alignment, we can understand and study organisms, find the correlation between their structure and function, study the evolution and evolution of organisms based on the comparison results of biological network data, and transfer knowledge between different networks. With unknown organisms, we study un-known organisms.
At present, most of the research work is only for a specific problem or application, the time complexity of the algorithm is high, and the algorithm is inefficient. The aim of the research on alignment models and algorithms of biological networks is to develop a general-purpose alignment software, which can efficiently align multiple biological networks with multiple application patterns, similar to the sequence alignment software BLAST.
At the same time, the Systems Biology Markup Language (SBML), a representation and standard format representing many different classes of biological phenomena, including metabolic networks, cell signaling pathways, is frequently used and visualized. There are currently three LEVELs of SBML defined. SBML is defined in LEVELs. However, each LEVEL can have multiple VERSIONs within it, and new VERSIONs of a LEVEL dosupersede old VERSIONs of that same LEVEL. Therefore, it is necessary to be compared in different versions.
Using Metlab, researchers are likely to find some interesting genes through network comparisons to establish their own metabolic pathway. Therefore, we present them with the gene editing section. The gene of interest can be easily analyzed in different aspects. Certainly, the analysis of a chromosome or a plasmid is also available. Different from the traditional NCBI database, this section is nicely visualized and functionally improved.