Systems Biology and Computer-Aided Drug Discovery

Timothy   G.   Lilburn; Yufeng      Wang

doi:10.2174/157340906778226445

Abstract

With the advent of the post-genomic era, systems biology has set the stage for a quantum leap in our understanding of the fundamental cellular processes, phenotypic variations, and disease mechanisms. By integrating the vast amount data from genomics, transcriptomics, and proteomics analyses, systems biology seeks a holistic view of organisms and the modules that compose them. The approaches to a systems level view of biology can be broadly classified as either deterministic or probabilistic. The former approach generates highly detailed views that are mechanistic and often quantitative, while the latter type of approach generates high level views that have usually been qualitative. An ability to see and describe the systems-level processes in the organism and thereby identify potential vulnerabilities could expedite the process of drug discovery and development. A systems biology approach will aid in at least four of the major stages of this process: target identification, target validation, preclinical testing and clinical trials. We give an overview of systems biology and describe, with examples, how it is being used for drug discovery and development.

Keywords: Systems biology, drug discovery, data analysis, networks, deterministic modeling, probabilistic modeling, models, pharmaceuticals