Knowledge discovery for stochastic models of biological systems

Biology is the science of life and living organisms. Empowered by the deployment of several automated experimental frameworks, this discipline has seen a tremendous growth during the last decades. Recently, the focus towards studying biological systems holistically, has lead to biology converging with other disciplines. In particular, computer science is playing an increasingly important role in biology, because of its ability to disentangle complex system level issues. This increasing interplay between computer science and biology has lead to great progress in both fields and to the opening of new important areas for research. In this thesis we present methods and approaches to tackle the problem of knowledge discovery in computational biology from a stochastic perspective. Major bottlenecks in adopting a stochastic representation can be overcome with the use of proper methodologies by integrating statistics and computer science. In particular we focus on parameter inference for stochastic models and efficient model analysis. We show the application of these approaches on real biological case studies aiming at inferring new knowledge even when a priori (and/or experimental) information is limited.