Sensitivity analysis for inferring properties of deterministic and stochastic models

In systems biology several mathematical models have been developed. Given the complexity of biological processes, researchers are often in presence of models in which the systems dynamics are not foreseeable. In fact, the behaviour of these models depends on the values of several input parameters. In this context, it is becoming increasingly important the ability to perform sensitivity analysis, which allows to identify the systems behaviour in relation of the systems input parameters. Through sensitivity analysis it possible to identify which are the key features for a particular model property. In this work we show how the use of sensitivity analysis can be useful to highlight the systems dynamics of both deterministic and stochastic models. In particular, two sensitivity analysis examples have been performed as case studies: the first related to a deterministic model about calcium signalling in neuron, while the second concerns a stochastic model about bacterial chemotaxis.