Towards Cellular Systems in 4D

Systems Biology: Hope versus Hype The approaches to and therefore the definitions of systems biology have been varied. These range from collections of physiological data with quantified molecular parts lists (e.g., genes, expression levels, localizations) to abstract mathematical modeling of biological processes. The scale at which the discipline of systems biology focuses on a specific question or problem is also a matter of contention: a tiny protein can be a complicated biological system (e.g., we still do not fully understand how a protein folds) and, as is obvious, an entire ecosystem with thousands of species constitutes another type of complex biological system. The term “systems biology” will probably get further diffuse as it is now under the limelight and new funding opportunities will be available to very diverse scientific communities. Irrespective of whether there is a consensus on the definition of the term or whether it remains fuzzy, “systems biology” aims at a quantitative understanding of biological systems to an extent that one is able to predict systemic features. It is expected that this understanding will allow for rational design and permit us to modify the behavior of biological systems (i.e., synthetic biology). This applies to any system comprised of biological components that is more than the mere sum of its components or, in other words, the addition of the individual components results in systemic properties that could not be predicted by considering the components individually. What do we hope to accomplish with modern systems biology approaches? Implicitly, there are a number of steps involved, each of which contributes to greater biological understanding. A typical process pipeline starts with standardized quantitative and qualitative data collection, archiving, and management. It is followed by proper integration of the data allowing comparative evaluation (this can reveal biases in data collection and indicate generalizable or specific features of the system studied). A next step would be the idealized reconstruction of the experimental situation as close to reality as possible. This is the preposition for studying the internal consistency of concepts and would allow a more global interpretation of the experimental data. An important component is the ability to generalize from the experimental setup to the system under study which includes testing of the consequences. Even more, we expect to extrapolate to concepts that are inaccessible to current experimentation and hope to arrive at novel concepts that are not deducible from the details.