Abstract
Systems biology focuses on the roles of cellular pathways and networks rather than single biomolecules to describe biological function. A systems view of biology requires technology that can generate and quantitatively analyze, large multi-dimensional data sets from many different sources. New technology has made this approach to drug discovery increasingly feasible. Detailed changes in cellular phenotype can be quantitatively measured using high content phenotypic screens. Changes in a cells entire transcriptome or proteome can be profiled in detail. Libraries of small molecules, peptides or poly-nucleotides such as siRNA can be screened to identify perturbagens that modulate transcriptomic, proteomic and cellular phenotypic signatures. These molecular agents can be used to deconvolute pathways and networks. The power of these technologies lies in their ability to generate complex biological data at massive scales. Integration and analysis of this multi-parametric data is vital to systems biology research. Patterns and relationships within these data sets can be revealed using factor and principal component analysis. These patterns can point to pathways that are relevant to specific biological processes making the ultimate goal of understanding the biology of a cell at the systems level possible.
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