Computational approaches to model the cell cycle, a complex biological system

The main goal of this thesis was to investigate possible computational approaches useful to study the cell cycle, a biological complex systems. The complexity of this biological process mainly relies in the number of components (genes and proteins), their interactions and regulations, from which the global activity of the system depends. In this context I proposed a bioinformatic and computational framework for the investigation of various aspects of the cell cycle. On the one hand a solution useful to deal with the complexity of the huge amount of data concerning the cell cycle, essential for carry on the research in a systems biology perspective has been implemented. On the other hand, I faced the complexity related to the cell cycle modeling and I selected two different methods to simulate the same process, a mathematical deterministic one and a computational hybrid one. The first step of this thesis was the development of a resource, the Cell Cycle Database, which integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behavior of each component. Afterwards, two different modeling approaches were chosen to cope with the complexity of the same biological pathway, that is the G1 to S transition in the mammalian cell cycle process. The former is a deterministic mathematical approach, based on ordinary differential equation systems, the latter is an hybrid computational approach, based on the double nature of this biological process which results from the combination of continuous dynamics and discrete events. Moreover this thesis faced with the parameter estimation, a crucial problem in the modeling process which is related to the complexity in determining the constant values involved in the kinetic reactions of the models. An automated parameter estimation tool was implemented on cell cycle models and it is embedded in the cell cycle integrated system. In conclusion, the research presented in this thesis concerns two fundamental aspects in computational systems biology: the data integration useful to understand in depth the biological process under investigation and the development of mathematical and computational models for the cell cycle processes. In this context I proposed a set of interesting challenges related to mathematical modeling in biology, such as the definition of the mathematical model which best fits with experimental biological data, the analysis of emergent properties of the modeled biological system, the application of alternative and innovative modeling techniques to approach the simulation of this complex system.