Uncertainty and sensitivity analysis of biological delayed models with noise

Description

The knowledge of biological phenomena is often incomplete because experimental measures are lacking and variances in many of the parameters values occur due to extensive variability in the data. Uncertainty and Sensitivity Analysis helps to characterise and control these uncertainties associated with a model. Uncertainty analysis (UA) quantities the uncertainty in the outcome of a model produced by the uncertainty in parameter inputs. Sensitivity Analysis (SA) has the complementary role of investigating what parameters contribute most to the outcome value. Several methods have been developed to deal with US analysis: differential analysis, response surface methodology, Monte Carlo (MC) analysis, and variance decomposition methods. For the purpose of this work we are interested in the Latin hypercube sampling (LHS) method for what concerns the uncertainty analysis and in the Partial Rank Correlation Coefficient (PRCC) and Extended Fourier amplitude test (eFAST) for the sensitivity analysis. We implement Matlab codes to perform the uncertainty and sensitivity analysis of stochastic models with delay. The uncertainty in the model output generated from uncertainty in parameter inputs is investigated. We obtain a classification of suitable thresholds of the delays to predict the stability switch of the positive equilibrium point. We apply our algorithm to biological stochastic model with memory terms and compare our results with the deterministic counterparts. Finally it is provided a discussion of biological implications. This is a joint work with Margherita Carletti (Institute of Biomathematics, University of Urbino, Italy) and Gaetano Zanghirati (Department of Mathematics, University of Ferrara, Italy).

Period	12 Dec 2011
Event title	Workshop on "Numerical Analysis of Multiscale Problems & Stochastic Modelling"
Event type	Conference
Location	AustriaShow on map