Parameter sensitivity analysis of stochastic models: Application to catalytic reaction networks

A general numerical methodology for parametric sensitivity analysis is proposed, which allows to determine the parameters exerting the greatest influence on the output of a stochastic computational model, especially when the knowledge about the actual value of a parameter is insufficient. An application of the procedure is performed on a model of protocell, in order to detect the kinetic rates mainly affecting the capability of a catalytic reaction network enclosed in a semi-permeable membrane to retain material from its environment and to generate a variety of molecular species within its boundaries. It is shown that the former capability is scarcely sensitive to variations in the model parameters, whereas a kinetic rate responsible for profound modifications of the latter can be identified and it depends on the specific reaction network. A faster uptaking of limited resources from the environment may have represented a significant advantage from an evolutionary point of view and this result is a first indication in order to decipher which kind of structures are more suitable to achieve a viable evolution.

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► Methodology for parameter sensitivity analysis of discrete stochastic models.
► Impact of parameters on the distribution probability of the model output.
► Sensitivity assessed in different regions of the parameter space.
► Parameters ranked according to the sensitivity.
► Procedure performed on a model of protocell.