Mathematical modeling of biochemical networks has attracted considerable attention in recent years and is recognized as one of the major challenges facing the biology research community today. Most of the models available in the literature can be classified into two families: models with purely continuous dynamics (e.g. models for the evolution of concentrations of proteins in terms of ordinary differential equations) and models with purely discrete dynamics (e.g. graph models of the interdependencies in a regulatory network, Boolean networks). Even though it is widely recognised that many biochemical processes involve both discrete and continuous dynamics, there have been very few attempts to establish links between these two classes. The objective of the group is to exploit recent developments in the area of hybrid systems to address open problems in modeling and analysis of biochemical networks.
One of the defining changes in molecular biology over the last decade has been the massive scaling up of its experimental techniques. The sequencing of the entire genome of organisms, the determination of the expression level of genes in a cell by means of DNA micro-arrays and the identification of proteins and their interactions by high-throughput proteomic methods have produced enormous amounts of data on different aspects of the development and functioning of cells. A consensus is now emerging among biologists that to exploit this data to its full potential one needs to complement experimental results with formal models of biochemical networks. Mathematical models that describe gene and protein interactions in a precise and unambiguous manner can play an instrumental role in shaping the future of biology. For example, mathematical models allow computer-based simulation and analysis of biochemical networks. Such in silico experiments can be used for massive and rapid verification or falsification of biological hypotheses, replacing in certain cases costly and time-consuming in vitro or in vivo experiments. Moreover, in silico, in vitro and in vivo experiments can be used together in a feedback arrangement: mathematical model predictions can assist in the design of in vitro and in vivo experiments, the results of which can in turn be used to improve the fidelity of the mathematical models.