What is Systems Biology?
Systems Biology refers to a type of biological research that is firmly based on a model of a biological system. It aims to use that model to explain and simulate the behaviour of a biological system, and to use simulations to help design experiments to challenge, expand and refine that model.
There are many definitions of systems biology, ranging from: "a research approach that seeks to describe the overall behavior of a biological system through detailed, quantitative experimentation combined with conceptual or computational modeling of the systems components and their interactions" (see SysBioSIG), to the very concise: "the study of biological function that derives from interactions" (courtesy of Westerhoff).
Systems biology aims to integrate the widest possible range of data about a biological system, and for that it needs a broad variety of research disciplines to collaborate. It signifies a fundamental shift in scientific philosophy; from the traditional reductionist towards an integrative approach.
Whereas reductionism – isolating biological entities and studying these in-depth – has brought innumerable scientific triumphs, systems biology aims to extend and augment this by studying living organisms as interacting networks of these entities, observing that such interactions play the defining role in what life really is. Contained in this is the very definition of emergent properties – that the living cell is significantly more than the sum of its components.
On a more specific level, systems biology relies heavily on recent technological advances, most notably the so-called -ome (global) technologies, enabling simultaneous studies of all cell components belonging to a biochemical class – the genome (DNA), transcriptome (RNA), proteome (proteins) and metabolome (low-molecular metabolites). Integration of this knowledge by the use of computational modelling should eventually allow the construction of fully predictive mathematical models of cellular behaviour. The potential impact on society of such predictive models, e.g. within medicine and industrial biotechnology, promises to be revolutionary.