Dry work in a wet world: computation in systems biology

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Abstract
Mol Syst Biol. 2: 40 Prior to the outburst of molecular genetics in the latter part of the past century, studying biological systems in their whole was commonplace owing to the limited scientific knowledge and appropriate molecular tools available. The molecular understanding of each observable phenotype was uncertain and based on empirical deductions from complete systems (Von Bertalanffy, 1950; Kacser and Burns, 1973). Large‐scale molecular biology has led to the routine deciphering of genome sequences and the subsequent identification of gene products and metabolites. Using these molecular reagents, thousands of studies have drawn novel molecular mechanisms, defined signalling cascades and molecular interactions. However, despite the ever‐increasing ability to catalogue the players in biological systems, the relationship between overall behaviour of the biological system and the newly discovered molecular mechanisms remains often puzzling and elusive. This is relevant not only to the general understanding of biology but also to its application in understanding human disease. The production of a ‘disease’ phenotype is the result of many interacting components, from ‘simple’ monogenic diseases through to complex disease with multiple genetic and environmental factors. If we wish to define the molecular targets appropriate for therapeutic intervention, develop better diagnostics and understand how environmental factors influence disease, we must return to the study of complete biological systems armed with the ever expanding catalogue of molecular reagents. Systems biology is a new term for the old science of studying biological systems holistically, but now reinforced with high‐throughput, increasingly affordable molecular tests and considerable sophistication in computational modelling (Kitano, 2002). We will focus here on the informatics of systems biology and how this rediscovered discipline is changing the way computers are used in molecular biology. Organized merging of computational systems biology with experimental systems biology is the most important challenge facing modern laboratories. We …