Since recent years the work on biological and metabolic network has been increasing due to the new biological discoveries and essential metabolites. Metabolomics being a burgeoning field, which produces voluminous data that, like other ‘omics’ data, should be seen as a resource that contributes specifically to the former half of an iterative cycle of hypothesis-generating and hypothesis- testing phases. It is becoming increasingly apparent that our ability to generate large quantities of metabolomics or metabolic profiling data will help to open up many previously inaccessible areas of biology various high-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. With the study of enzymes and metabolites new pathways can be discovered, which can help in the analysis of the various process taking place in the organism. In order to identify potential drug targets the concept of choke points was used to find enzymes which uniquely consume or produce a particular metabolite. Hence the study of these choke are taken into consideration.

The vast amounts of biological data present in standard repositories are the heart of bioinformatics today. This has been possible due to the sequence alignment, microarray, etc. approaches over the years. The huge biochemical networks have certain assemblies of modules called the reaction motifs. There are different types of such motifs in a network and it is of foremost importance to identify such motifs and get insights of their functions and regularities. It is also required study the intensity of occurrences of these motifs to establish certain evolutionary and functional relationships in and amongst pathways. The inference that these motifs have been selected for function rests on the idea that their occurrences are significantly more frequent than random. Such motifs have not only been identified in a wide range of networks across many scientific disciplines and are suggested to be the basic building blocks of most complex networks.