Abstract

Biological regulatory systems are complex due to their role in living organisms in modulating precise responses to changes in internal and external conditions. In this respect, mathematical models have become essential tools to address their complexity for a better understanding of their mechanisms. The vision here, based on integrating experimental and theoretical techniques, provides a systematic means to quantitatively study the characteristics of the interactions that occur in living organisms. The outcome of such an endeavour should provide insights in terms of predictions and quantifications for further investigations in systems and synthetic biology. In this thesis, we establish an integrated modelling framework that can ensure the interaction of experimental biology with the development of quantitative mathematical descriptions of biological systems. To this end, we develop a framework to simulate and analyse biological regulatory systems by integrating different layers of regulatory information. The work herein presents a biological model development workflow in terms of a step by step approach, highlighting challenges and “real life” problems associated with each stage of model development. In the first part, we have focused on applying systems and synthetic biology modelling tools to the phosphate system at the cellular and genetic levels in Escheria coli. Then, we have analysed the interaction mechanisms and the dynamic behaviour of the phosphate starvation response deactivation and evaluated the role of phosphatase activity. We have investigated how the properties of these signalling systems depend on the network structure. Moreover, we have constructed detailed transcriptional regulatory network models and models for promoter design. In the second part, we have designed a multi-level dynamical set up by providing a novel closed loop whole body model of glucose homeostasis coupled with molecular signalling. We have then developed a system embracing the intracellular metabolic level, the cellular level involving the dynamics of the cells, the organ level, and the processes within the whole body. The output of each model directly has been fed with the variables and the parameters of the next aggregated model. This allowed us to observe the metabolic changes that occur at all levels and monitor inter-level communications for Type 2 Diabetes disease.