Boolean Network Models of the Fission Yeast Cell Cycle and Apoptosis

Abstract

Gene and protein regulatory networks guide all functions in cells and are very complex. Most mathematical approaches for predicting the evolution over time of these networks have a common challenge - a demand of detailed information about the system, that is for example knowledge of exact concentrations and kinetic constants for the differential equation approach. In this thesis we show that Boolean models are able to reproduce sequential patterns of protein states with no demand on kinetic constants and exact concentrations. We demonstrate this on an example of a general model of apoptosis for human cells and of cell cycle of the simple eukaryote fission yeast (Schizosaccharomyces Pombe).A general model of apoptosis is constructed on available data from biochemical databases. The dynamical properties of the obtained model indicate that apoptosis is a sufficiently robust process, since the system starting from different initial states reaches a fixed point that corresponds to the death of the cell. The model is verifiedvia deleting a number of important proteins and observing the changes in apoptosis rate. The obtained results qualitatively reproduce observations in experiments.The second model, Boolean model of fission yeast cell cycle, is also based merely on known biochemical reactions. The model is able to reproduce the wild-type sequence of events during main cell evolution phases. The dynamical properties of the model indicate that the wild-type cell network has a dominant attractor in state space that coincides with the biological stationary state, called G1.The consistence of the model is tested on its response to different damages such as mutations. The tests indicate that the Boolean network model captures a large number of single, double, triple loss-of-function and overexpressed mutations.In the last part of this thesis we set two approaches - differential equations and Boolean networks in relation to each other with the same example system, the fission yeast cell cycle. We found that the Boolean network can be formulated as a specific coarse-grained limit of the more detailed differential network model for this system. This lays the mathematical foundation on which Boolean networks can be applied to biological regulatory networks in a controlled way. The limitations of the Boolean approach are also discussed.The results of this thesis support the idea that the nature of the fission yeast cell cycle is discrete to some certain degree and that the timing is not always a crucial factor. Therefore, qualitative data may be sufficient to grasp certain parts of control mechanisms of biological processes.