The main aim of this thesis is the design and implementation of a large-scale model of eukaryotic (specifically human) signal transduction. The signal transduction system is an important component in the regulation of homeostasis and cellular response to stimuli. Defects within this system are implicated or causal in a host of diseases. Existing models focus on relatively small subsets of the signal transduction system due to their need for reaction rate data, which are not widely available. This prevents or biases the simulation of many events of interest, and limits the utility of these models in investigating cellular behaviour.
Here we present an overview of the available modelling techniques, assessing their suitability for large-scale modelling given currently available experimental and computational methods. We then proceed to design and implement the first model framework capable of simulating large-scale signal transduction systems. We then further assess the available data, identifying a number of structural and design issues within the available databases of signal transduction that hinder modelling. Finally, we implement and generate a predictive model of approximately 32% of known human signal transduction, using the data contained in the Reactome database.
In doing so, we find that there are methods of simulating signal transduction at a cellular scale that predict phenotype with high accuracy. We show that models using these techniques are useful in exploring the possible phenotypes of cells in an experimental setting. Finally, we outline methods for future expansion of the model to capture behaviour resulting from the effects of changes to transcription and translation on the signal transduction systems.