DDT • Volume 10, Number 9 • May 2005 Reviews • DRUG DISCOVERY TODAY: BIOSILICO 653 www.drugdiscoverytoday.com Over the past several years there has been a paradigm shift in life science research as a result of the unprecedented advances in several laboratory tech- niques, such as automated DNA sequencing, global gene expression measurements, and proteomics and metabonomics techniques. The high throughput data collectively referred to as ‘OMICs’ data are ubiq- uitous throughout the drug discovery pipeline from target identification and validation to the development and testing of drug candidates. However, OMICs data are poorly utilized because of the lack of adequate methods for interpretation in the context of disease and biological function. Although bioinformatics has developed robust statistical solutions for evalua- tion of the significance and clustering of data points, statistics alone does not explain ‘the underlying biology’. The complexity of our own biology requires a sys- tem-wide approach to data analysis, which can be defined as the integration of ‘OMICs’ data using computational methods [1]. It is clear from the field that the identification of the ‘part list’ of all the genes and proteins is insufficient to understand the whole. It is the assembly of these parts (the general schema, the modules and elements) and the dynamics of changes in response to stimuli that is truly the key to understanding life, form and function [2,3]. The assembly of ‘cellular machinery’ is most effectively presented as the ‘interactome’, the network of interconnected signaling, regulatory and biochemi- cal networks with proteins as the nodes and physi- cal protein–protein interactions as edges [4,5]. As in many fields of science, technology and social life, the topology and dynamics of complex networks can be studied by graph theory [5]. The information about protein interactions has been collated from the vast amount of published experimental data annotated and assembled in interactions databases. Network data analysis tools are now commercially available and robust enough for simultaneous pro- cessing of multiple ‘whole genome’ data files, such as human expression microarrays. Recently, the inter- pretation of experimental ‘OMICS’ datasets in the context of accumulated knowledge on human func- tional networks was described as the first step in studying complex systems [2,6]. Now, we can con- sider the building of the basic framework, databases and logistics needed to accomplish this. Networks- centered data analysis is well underway in the major pharmaceutical companies. In this review, we will Yuri Nikolsky* Tatiana Nikolskaya Andrej Bugrim GeneGo, 500 Renaissance Drive, #106, St. Joseph, MI 49085, USA *e-mail: yuri@genego.com Biological networks and analysis of experimental data in drug discovery Yuri Nikolsky,Tatiana Nikolskaya and Andrej Bugrim Cellular life can be represented and studied as the ‘interactome’ – a dynamic network of biochemical reactions and signaling interactions between active proteins. Systemic networks analysis can be used for the integration and functional interpretation of high-throughput experimental data, which are abundant in drug discovery but currently poorly utilized. The composition and topology of complex networks are closely associated with vital cellular functions, which have important implications for life science research. Here we outline recent advances in the field, available tools and applications of network analysis in drug discovery. 1359-6446/05/$ – see front matter ©2005 Elsevier Ltd. All rights reserved. PII: S1359-6446(05)03420-3 REVIEWS