Modelling and analysis of biological systems is crucial in order to quantitatively explain and predict their behaviour. The importance of modelling in systems biology becomes even more evident when tackling complex, large systems. Approaches that rely on modelling can be used to tackle a range of problems, such as cellular signalling or using genetically modified bacteria to synthesize human proteins for medical purposes. These phenomena range across different spatiotemporal scales, whether it is predicting the decay of red blood cells for blood transfusion or finding engineering targets in order to increase the productivity of bioprocesses. Reconciling models with experimental data and quantifying how uncertainty propagates through these models is crucial to exploiting them to their full potential. Uncertainty impacts not only the calibration and validation of a model, but also the predictions extracted from it. Developing and implementing uncertainty quantification methods into these models is a key step in exploring their behaviour and accelerating the adoption of such methods in metabolic engineering.

In this thesis I firstly propose to implement and explore uncertainty quantification methods on control coefficients obtained by Metabolic Control Analysis (MCA) of Genome Scale Metabolic Models (GEMs), with the goal of quantifying uncertainty propagation from model parameters to control coefficients. I additionally want to build Single Protein Models that describe the effects of Post Translational Modifications (PTMs). The nature of the experimental data available to calibrate these models makes uncertainty an inherent property, and therefore Global Sensitivty Analysis (GSA) techniques are well suited to describing the links between parameter uncertainty and model behaviour.