Computer Simulation of a Large-Scale Signalling Network Regulating Translation Initiation in the Mammalian Cel

The process of translation initiation in mammalian systems is complex and not fully understood. It is regulated by an intricate network of signalling pathways and is a significant energetic burden to the cell. Although models of initiation are available for yeast, to date, such models do not include the regulation of this process, nor do they exist for mammalian systems. Existing literature was used to reconstruct the process of translation initiation and the regulatory signalling networks in the Petri Net formalism within the software Snoopy. The final version of the model was altered to incorporate the effects of Murine Norovirus. The model was converted to a binary form and the software QSSPN was used to run Gillespie algorithm-based stochastic simulations. The predictive power of the model was established by incorporating commonly used chemical inhibitors. Using the Matthews' Correlation Coefficient, a quantitative measure of predictive power was established by comparing the model behaviour to the effects of each inhibitor recorded in existing literature. A qualitative model containing 584 reactions was constructed. The predictive power of the model was raised to MCC = 0.4558 through a series of refinements. Two predicted behaviours, an increase in eIF4E phosphorylation and a reduction of AKT phosphorylation both in response to Rapamycin, were validated with the Immunoblotting techniques, Western Blotting and Human Phospho-MAPK Arrays, in the murine monocyte/macrophage RAW 264.7 cell line. The model incorporating the effects of Murine Norovirus infection generated five testable predictions. Of these, four were verified with the Human Phospho-MAPK Arrays. The model presented here demonstrates the value of generating large-scale models using the binary model formalism and performing simulations with QSSPN. The model of the regulation of translation initiation has shown that it is capable of generating experimentally verifiable predictions. Furthermore, the incorporation of viral effects demonstrates that the model has a range of potential future uses.