Cancer-signalling networks are typically complex which involves gene regulation, signalling, cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Computational model of networks make possible to understand the complex behaviour of cancer-signalling network. Correlation between complexity (clustering coefficient) of cancer-signalling network pathway and Cancer Epidemiological data sets (Cancer incidence, Death rate and lifetime risk of cancer) has been validated. Results of study support the initial assumption, that the complexity of network matrices is a direct indicator of cancer threat. Understanding the differential behaviour of regulatory networks during health, disease and in response to drugs play a crucial role to enhance drug development efforts, new target identification, delineation of off-target effects, methods of disease prediction, combinatorial drug regimens and also in development of molecularly targeted personalized treatment.
The eukaryotes sterol pathways are extremely conserved and these biosynthetic pathway are very long which includes the synthesis of dolichols, coenzyme Q, heme A, and isoprenylated proteins.14-Demethylase is an essential enzyme of the cytochrome P450 superfamily, which is potential the target of azole antifungals. Predicted results shows that 14-alpha sterol demethylase have molecular weight of 58930.8 Daltons and the theoretical isoelectric point (pI) of 7.64. The negative Grand average of hydropathicity (GRAVY) index of ‐0.125. The Aliphatic index of Aspergillus fumigates 14-alpha sterol demethylase is 89.48. Alpha helix (Hh) accounts 210 amino acids of about 40.08%. The extended strand (Ee) had 91 amino acids accounting 17.37, Beta turn (Tt) made up of 51 amino acids making up 9.73% and random coil (Cc) made up of 172 amino acids accounting 32.82%. The subcellular localization of 14alpha sterol demethylase Cyp51B was predicted to be a Plasma membrane protein.