Systems Biology is a young discipline that combines aspects of biology, mathematics, engineering, and physical and computer sciences to focus on the systematic study of complex interactions in biological systems. It considers the system as a whole rather than a sum of the parts for creating an integrative image of complex life processes by mathematical simulation – from single cells up to complete organisms.

Our research goals are to contribute to the elucidation of mechanisms underlying basic cellular processes, evolution and interactions of microbes with each other and with their hosts, and to translate this knowledge into applications of biotechnological and biomedical relevance. The strategic concept is to develop and apply theoretical frameworks supporting (and relying on) experimental research for understanding, from a Systems Biology perspective, the various processes and hierarchies of cellular networks as well as interrelationships among prokaryotes and of prokaryotes with their environments (e.g. of Pseudomonas), including the host.

Ultimately, we aim at using these frameworks for directed cellular re-programming and for the rational design of experiments combining mathematical modeling with experiments at all stages. Cellular re-programming, under the broader umbrella of Synthetic Biology, plays an increasingly important role in our research. Synthetic biology can be viewed as technological, a design counterpart to Systems Biology, and draws on knowledge developed in biology, robotics and adapting engineering design principles stemming from Information Technologies. A medium-long term goal is the coupling of the knowledge generated on the quantitative understanding (modeling) of cellular processes in prokaryotes with the development of mathematical frameworks that integrate the essential features of host-pathogen interactions.

In the long run, it is expected that the expertise and knowledge acquired will lay the foundation for the establishment of solid "in silico" models which, by relying on "virtual patients" and "virtual infections/drug targets", can contribute to i) elucidation of key complex mechanisms underlying infection and immune responses, and ii) a reliable and predictive method to increase efficiency and productivity across the drug discovery and development pipeline. Using virtual patients and virtual drugs/targets, in silico R&D simulates experiments via computer, rapidly testing what would likely take months or years in the laboratory or clinic, greatly reducing product development costs, and possibly allowing targeted drug development.