Toolkit for Reverse Engineering of Molecular Pathways via Parameter Identification
This thesis is a contribution to the field of systems biology, which is concerned with mathematical and computational modelling of biological systems. The aim of the field is to understand biological processes via holistic computational methods. One of the standing problems in systems biology is how to derive model of a system, preferably one easily understandable by humans, from experimental data and observations. Understandably, the structure of the problem depends heavily on the system of interest and the available data, therefore it is worthwhile to create new methods that utilize particular features, as there can hardly be a universal solution. Here we present an approach for modelling and analysis of complex biological networks that uses a high-level, abstract modelling framework---the multi-valued logical networks. In this framework we employ an automated method originating in the theoretical computer science, called model checking, which allows for formal reasoning about dynamical systems. We can then create a multitude of candidate models and use model checking method to compare the behaviour of these to experimental data. Our approach however produces high volumes of data. To be able to work with the data we use basic statistical methods, which allow us to summarize the dataset into a few key values. In addition, these values can be subsequently compared between multiple datasets. For better understanding we couple these methods with an interactive visualization software. The whole framework is implemented in a tool called TREMPPI, which is available under an open-source license and distributed together with this thesis. We illustrate the functions of TREMPPI on three biological studies---two human signalling pathways, related to cancer, and a protection mechanism of the bacteria E. Coli.