Sensitivity analysis in Petri net representation of biological systems

Research in proteomics has opened a new door to applications of engineering tools and techniques in biology. Particularly modelling systems and control design are becoming more and more important in drug discovery, diagnosis and prognosis of diseases. Some of the biggest challenges however remain to be the unprecedented size and complexity of biological systems. This paper studies sensitivity analysis in Petri net models of biological systems. A theory is developed for efficient calculation of an impotency measure as an indication of inverse sensitivity. It is also shown using simulation that the measure accurately identifies the best candidate for drug design target in Caspase cascade.