From Complexity of Biological Systems to Systems Biology and Systems Medicine

The information explosion in biology has not resulted in a true understanding of biological systems in the sense that useful predictions can be made. Systems biology is the logical step following the information explosion mostly through genomics type of analyses of biological systems. Systems biology exploits the iterative cycle of at the one hand experimentation that is driven by quantitative and predictive models and on the other hand data integration and system analysis based on data-driven modeling. A major hurdle is the extreme complexity of biological systems. Systems biology addresses this issue by integrating diverse types of biological information in computer-based models that integrate information, can be interrogated about system behavior and allow the uncovering of underlying system principles. Biologists and biomedical investigators are generally not well equipped to cope with the complexity hurdle. Therefore, they team up in the systems biology field with physicists and engineers, which are used to translating experimental data into computer models and are able to work with complex systems. At the same time mathematicians play a crucial role in developing the necessary methodologies for the identification and analysis of mathematical models. Large networks, such as social networks, computer and biological networks, consisting of thousands to millions of vertices, have recently attracted much attention. Biological networks, including protein-protein interaction networks, gene regulatory networks, and metabolic networks, are among those most widely studied. In order to extract meaningful information from the vast amount of data encrypted in the networks, powerful methods for computational analysis need to be developed.