Background Intrinsic pathway of apoptosis is generally mediated by BCL-2 (B cell lymphoma 2) family of proteins; they either induce or inhibit the apoptosis. Overexpression of BCL-2 in cancer cell may lead to delay in apoptosis. BCL-w is the pro-survival member of the BCL-2 family. BCL2L2 gene is present on chromosome number 14 in humans, and it encodes BCL-w protein; BCL-w protein is 193 amino acids residues in length. Interactions among the BCL-2 proteins are very specific. The fate of cell is determined by the ratio of pro-apoptotic proteins to pro-survival proteins. BCL-w promotes cell survival. Studies suggested that overexpression of BCL-w protein is associated with many cancers including DLBCL, BL, colorectal cancers, gastric cancers, and many more. The cause of overexpression is translocations or gene amplification which will subsequently result in cancerous activity. Process For in-silico analysis, BCL2L2 gene was retrieved from UniProt (UniProt ID: Q92843). 54 missense variants have been collected in BCL-w proteins from COSMIC database. Different tools were used to detect the deleteriousness of the variants. Result In silico mutational study reveals how the non-synonymous mutations directly affect the protein’s native structure and its function. Variant mutational analysis with PolyPhen-2 revealed that out of 55 variants, 28 of the missense mutations was probably damaging with a score ranging from 0.9 to 1, while 24 variants were benign with a score ranging from 0 to 0.4. Conclusions This in silico work aims to determine how missense mutations in BCL-w protein affect the activity of the protein, the stability of the protein, and to determine the pathogenicity of the variants. Prediction of pathogenicity of variants will reveal if the missense mutation has a damaging effect on the native structure of protein or not. Prediction of protein stability will reveal whether the mutation has a stabilizing or destabilizing effect on the protein.

Chitosan as a natural biopolymer has been produced to be the important host for the preparation of metallic nanoparticles (MNPs) because of its excellent characteristics like:- good stabilizing and capping ability, biocompatibility, biodegradability, eco-friendly and non-toxicity properties. Chitosan can play a very important role for synthesis of metallic nanoparticles, as chitosan is a cationic polymer. It attracts metal ions and reduces them and also Capps and stabilizes. So basically chitosan can be responsible for the controlled synthesis of metallic nanoparticle. Chitosan has a very good chelating property. This property is due to its –NH2 and –OH functional groups. Size and shape of metallic nanoparticles are much affected by chitosan concentration, molecular weight, time of reaction, degree of acetylation of chitosan, pH of the medium, method of synthesis and type of derivative of chitosan etc. Metallic nanoparticles`s properties and applications are much associated with their size and shape. Optimization of the metallic nanoparticle size and shape has been the subject of curiosity for nanotechnology scientist. Chitosan can solve this problem by applying the optimization conditions. But a very little work is reported about: - how chitosan can affect the size and shape of metallic nanoparticles and how can it reduce metal salts to prepare metallic nanoparticle, stablilized in chitosan metrics. This is very first report as a review article highlighting the effect of chitosan on synthesis of metallic nanoparticles and optimization conditions. This review will also be beneficial for scientist working on food sensing application of nanoparticles.  Various synthesis methods and applications of chitosan based metallic nanoparticles have also been reported in details.

Abstract-The analysis and interpretation of relationships between biological molecules is done with the help of networks. Networks are used ubiquitously throughout biology to represent the relationships between genes and gene products. Network models have facilitated a shift from the study of evolutionary conservation between individual gene and gene products towards the study of conservation at the level of pathways and complexes. Recent work has revealed much about chemical reactions inside hundreds of organisms as well as universal characteristics of metabolic networks, which shed light on the evolution of the networks. However, characteristics of individual metabolites have been neglected in this network. The current paper provides an overview of bioinformatics software used in visualization of biological networks using proteomic data, their main functions and limitations of the software.

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A methodological framework of graph traversal in Systems Biology is presented here. At present there is need to investigate system rather individual component. The proposed analysis generalizes the various idea of network representations of protein interactions. This approach highlights various methods used in construction of protein interaction graph or network using suitable algorithm. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event.The analysis of the resulting network enables the importance of each protein for its interactions. Furthermore, the determination of the pattern of edge between residues yields insights into the function prediction of an interaction. This is of special interest to investigate intrinsically disordered proteins, since it is difficult to determine structural (threedimensional) architecture of each proteins in protein interactions network. In present work various approaches for protein interactions network construction, models and methods along with graph theories has been discussed which can be used to reveal hidden properties and features of a network. Further effective algorithm for visualization of protein interactions is suggested. As construction of Biological network is dependent on various properties of graph.A holistic approach such as Systems Biology approach can better solve the problem. This network profiling combined with knowledge extraction will help biologist to explore hidden information in genome as well as in proteome..