Sensitivity analysis for computational models of biochemical systems

Systems biology is an integrated area of science which aims at the analysis of biochemical systems using an holistic perspective. In this context, sensitivity analysis, a technique studying how the output variation of a computational model can be associated to its input state plays a pivotal role. In the thesis it is described how to properly apply the different sensitivity analysis techniques according to the specific case study (i.e., continuous deterministic rather than discrete stochastic output). Moreover, we explicitly consider aspects that have been often neglected in the analysis of computational biochemical models, among others, we propose an exploratory analysis of spatial effects in diffusion processes in crowded environments. Furthermore, we developed an innovative pipeline for the partitioning of the input factor space according with the different qualitative dynamics that may be attained by a model (focusing on steady state and oscillatory behavior). Finally, we describe different implementation methods for the reduction of the computational time required to perform sensitivity analysis by evaluating distribute and parallel approaches of model simulations.