Modelling and Inference Strategies for Biological Systems

For many years, computers have played an important role in helping scientists to store, manipulate, and analyze data coming from many different disciplines. In recent years, however, new technological capabilities and new ways of thinking about the usefulness of computer science is extending the reach of computers from simple analysis of collected data to hypothesis generation. The aim of this work is to provide a contribution in the Computational Systems Biology field. The main purpose of this recent discipline is to enhance the intertwined relationship connecting Biology and Computer Science, by developing tools and theoretical frameworks able to formally and quantitatively investigate the interactions among the components of biological systems. The final goal of these efforts is to assemble the different pieces into a working model of a living, responding, reproducing cell; a model that can be used for performing in-silico tests and simulations in order to understand and predict possible emergent properties. In this thesis we present the application to real biological case studies of a specific concurrent modelling language (derived by the metaphors of "molecules-as-object" -introduced by Fontana- and "cells-as-computations" -introduced by Regev and Shapiro- at the end of last century) and the development and implementation of a tool for inferring knowledge from experimental data in order to link the numerical aspects of a model to real wet-lab data