title: Parameter sensitivity analysis of stochastic models: application to catalytic reaction networks.
abstract: 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.
authors.affiliation: COSBI The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068 Rovereto (TN), Italy. chiara.damiani@unimib.it
authors.forename: Chiara
authors.initials: C
authors.lastname: Damiani
authors.forename: Alessandro
authors.initials: A
authors.lastname: Filisetti
authors.forename: Alex
authors.initials: A
authors.lastname: Graudenzi
authors.forename: Paola
authors.initials: P
authors.lastname: Lecca
mesh_terms: D020134:Catalytic Domain; D003198:Computer Simulation; D008954:Models, Biological