Currently biological data is warehoused in databases spread across the globe each database providing a different data set (proteins, nucleotide, metabolic etc.) of the same cell, tissue or organism. Each data set provides a different insight into the functioning of the whole cell. Integration of these data sets/databases provides a fundamental network behind the orchestra of the biological components at play, since the data is represented as a network, it is easier for mathematical operations affiliated to graph theory and network analysis to be carried out on these biological networks. The representation of biological components as a network, has paved the way for the application of graph theoretic studies on biological networks. Network alignment, is one such sub-area under network analysis, which has potential to help show that evolution is evident with respect to biology, is an expensive technique due to the inhibiting factor; subgraph isomorphism, which makes aligning large scale (typical biological network) a challenging feat. Outcome of such alignments can be used in the field of drug discovery, identification of keystone proteins, efficient designing of man-made networks etc. Network alignment on biological networks can reveal evolution of protein clusters (Local alignment) or network topology (Global alignment) that have stood the test of time and are functionally efficient. Network alignment can also help recognize key features among the networks unique to the organism, thus becoming a potential drug target in case of pathogenic organisms to humans/cash crops or livestock. Our research focus would be on the metabolic networks of Mycobacterium tuberculosis, we would carry out alignment studies of each network of mycobacterium along with networks of related prokaryotes and eukaryotes eventually leading to Homo sapiens. This will provide an evolutionary aspect of the network(s), as to how the network has evolved and also provide with difference or similarities between the networks of mycobacterium and humans. Hence network alignment on biological networks can be fruitful beyond comprehension.