Sensitivity analysis for inferring properties of deterministic and stochastic models (Contributo in atti di convegno)

Type
Label
  • Sensitivity analysis for inferring properties of deterministic and stochastic models (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Carlo Maj1,2, Ettore Mosca1,Ivan Merelli1 , Roberta Alfieri1, Dario Pescini3, Paolo Cazzaniga4, Giancarlo Mauri2, Luciano Milanesi1 (2011)
    Sensitivity analysis for inferring properties of deterministic and stochastic models
    in Sysbiohealth Symposium, Bologna, 14-15 dicembre 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Carlo Maj1,2, Ettore Mosca1,Ivan Merelli1 , Roberta Alfieri1, Dario Pescini3, Paolo Cazzaniga4, Giancarlo Mauri2, Luciano Milanesi1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1Istituto di tecnologie biomediche, CNR, Milano, Italy 2Università degli studi di Milano Bicocca, Italy Dipartimento di Informatica Sistemistica e Comunicazione 3Università degli studi di Milano Bicocca, Dipartimento di Statistica 4Università degli Studi di Bergamo, Dipartimento di Scienze della Persona,Italy (literal)
Titolo
  • Sensitivity analysis for inferring properties of deterministic and stochastic models (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-88-7395-696-9 (literal)
Abstract
  • 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. (literal)
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