The advent of the "-omics revolution" has forced us to reevaluate our ability to acquire, measure, and handle large data sets. Omic platforms such as expression arrays and mass spectrometry, with their exquisite selectivity, sensitivity, and specificity, are unrivaled technologies for detection, quantitation, and identification of DNA, messenger RNA, proteins, and metabolites derived from complex body tissue and fluids. More recently, attempts have been made to capture the utility of these platform technologies and combine them under the umbrella of systems biology, also referred to as pathway, network, or integrative biology. Applied systems biology is the integrated analysis of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. It necessitates the use of a variety of analytic platforms as well as biostatistics, bioinformatics, data integration, computational biology, modeling, and knowledge assembly protocols. Such sophisticated analyses may provide new insight into the understanding of disease processes and mechanisms of action of pharmaceutical agents. Ultimately, this requires a perspective on how complex systems behave and are modulated. In this regard, systems biology, more appropriately considered as a process containing a series of modules, aims to provide tools and capabilities to carry out such tasks. We describe the essentials required to carry out systems biology experiments, the method in which integrated data in the form of a systems biology correlation network affords new insight into understanding disease, and the vista of developing more efficient biomarkers and therapeutic agents.
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http://dx.doi.org/10.4065/79.5.651 | DOI Listing |
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