Received date: 08 May, 2016; Accepted date: 15 May, 2016; Published date: 22 May, 2016
Citation: El-Esawi MA, El-Ballat EM (2016) Integrative Systems Biology: Addressing Biological Processes and Analysis of High-Throughput Bioinformatics Data. Biochem Physiol 5:e148. doi:10.4172/2168-9652.1000e148
Copyright: © 2016 El-Esawi MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Systems biology is an approach that addresses cellular biological questions, and is widely used in the biosciences in a variety of contexts including the study of gene behavior and its biological pathways . The systems concept is mostly devoted to integrate data collection with computational and mathematical modelling in order to better understand biological processes. Methodology of integrating data into models is variable and multiple to better suit the system under analysis. This methodology includes an integration of mathematical and computational modelling, visualization tools and network inference. In addition, other aspects of bioinformatics, computer science and statistics are currently used in systems biology. These include the use of new aspects of computational models, such as constraint-based modeling and process calculi comprising stochastic π-calculus, Beta Binders, BioAmbients, BioPEPA, and Brane calculus. Systems biology could develop biological networks through analyzing high-throughput bioinformatics data . The results of each study could be enriched with other OMICS data constructed based on genomic, transcriptomic and proteomic studies. These data are deposited at the online databases and repositories for sharing data and models. In addition, the data can be accessed from bioinformatics databases comprising the cellular and molecular characteristics of biological network components . Other databases including BioMart and 12D may be used for enrichment studies . Different clustering models have been used to construct the biological networks. These models are used to form the network blocks or hubs . Following data annotation, data network could be constructed. Network analysis, however, relies on data integration, organization and topology characteristics [2,3].
Weighted correlation network analysis is commonly used for revealing and identifying clusters, modeling the relationship among clusters, calculating measures of cluster membership, characterizing intra-modular hubs, and for investigating cluster preservation. Two computational methods top-down and bottom-down models, are widely used in systems biology. The top-down computational model is applied to the large-scale high-throughput bioinformatics data including gene expression and protein results . However, small-scale data are clustered with the bottom-down model. Therefore, both of the primary data and understanding the system under investigation determine the clustering approaches .
Enrichment of the network components could be carried out through accessing bioinformatics databases or using the compatible plugins . In conclusion, in addition to the important role of cytological and molecular genetic approaches in crop improvement, systems approach enhances understanding the cellular mechanisms through integration of genome, transcriptome and proteome studies. It opens the door to better understand the gene regulatory system, molecular interaction, signaling integration, response networks and disease mechanisms [2-18]. The advanced bioinformatics packages would definitely enhance the applications of systems biology in revealing and understanding biological processes.
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