The emergence of proteomics, the large-scale analysis of proteins, has been inspired by the realization that the final product of a gene is inherently more complex than the gene itself. Shortfalls in the ability of bioinformatics and other related sciences to predict both the existence and function of genes have also illustrated the need for protein analysis. Better than gene sequencing or quantitative amplification, proteomics tools allow the study of disease phenotype by the detection and quantification of single protein in body fluids. Moreover, only through the study of proteins can posttranslational modifications be determined, which can profoundly affect protein function. Proteomics has been enabled by the accumulation of both DNA and protein sequence databases, improvements in mass spectrometry and the development of computer algorithms for database searching. In this review, it has been described why proteomics is important, how it is conducted, and how it can be applied to various spheres of pharmaceuticals and health sciences. It has been concluded that currently the most practical application of proteomics is the analysis of target proteins as opposed to entire proteomes

Monitoring agricultural activities has benefited so much over last 20 years from the advances in Remote Sensing (RS). A crop growth model holds a vital role in agricultural monitoring system. To run crop models are quite useful especially for prediction, however, the parameter determination in large area is in practical a difficult task. A method was proposed by Ines, (2002) to optimize the input parameters of a one-dimensional crop model (SWAP) by assimilating simulated evapotranspiration with remote sensing data. The optimization is based on GA (Genetic Algorithm). However, it requires huge computational time, which is one of the constraints in practical implementation of the method. Cluster is a type of parallel and distributed processing system, provides us with increased computing capabilities and which can help to remove the computational time constraints. Thus, a parallel crop model (SWAP-GA) procedures for remote sensed images is considered. In this study, a numerical experiment with three different SWAP-GA cluster implementation schemes is presented to show the strengths and limitations of these proposed approaches using Optima, Magi and Maeka clusters.