scholarly journals Novel global sensitivity analysis methodology accounting for the crucial role of the distribution of input parameters: application to systems biology models

2012 ◽  
Vol 22 (10) ◽  
pp. 1082-1102 ◽  
Author(s):  
Maria Rodriguez-Fernandez ◽  
Julio R. Banga ◽  
Francis J. Doyle
Keyword(s):  
Sensitivity Analysis ◽  
Systems Biology ◽  
Crucial Role ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity ◽  
Analysis Methodology ◽  
Input Parameters

scholarly journals Deeper understanding of non-linear geodetic data inversion using a quantitative sensitivity analysis

2005 ◽  
Vol 12 (3) ◽  
pp. 373-379 ◽  
Author(s):  
C. Tiede ◽  
K. Tiampo ◽  
J. Fernández ◽  
C. Gerstenecker
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Unknown Input ◽  
Volcanic Area ◽  
Unknown Parameters ◽  
Linear Inversion ◽  
Reliable Determination ◽  
Global Sensitivity ◽  
Non Linear ◽  
Input Parameters

Abstract. A quantitative global sensitivity analysis (SA) is applied to the non-linear inversion of gravity changes and displacement data which measured in an active volcanic area. The common inversion of this data is based on the solution of the generalized Navier equations which couples both types of observation, gravity and displacement, in a homogeneous half space. The sensitivity analysis has been carried out using Sobol's variance-based approach which produces the total sensitivity indices (TSI), so that all interactions between the unknown input parameters are taken into account. Results of the SA show quite different sensitivities for the measured changes as they relate to the unknown parameters for the east, north and height component, as well as the pressure, radial and mass component of an elastic-gravitational source. The TSIs are implemented into the inversion in order to stabilize the computation of the unknown parameters, which showed wide dispersion ranges in earlier optimization approaches. Samples which were computed using a genetic algorithm (GA) optimization are compared to samples in which the results of the global sensitivity analysis are integrated by a reweighting of the cofactor matrix in the objective function. The comparison shows that the implementation of the TSI's can decrease the dispersion rate of unknown input parameters, producing a great improvement the reliable determination of the unknown parameters.

A comprehensive global sensitivity analysis using generic sampling designs by means of a combination of variance- and distribution-based approaches.

2020 ◽  
Author(s):  
Gabriele Baroni ◽  
Till Francke
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Present Contribution ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Model Response ◽  
New Strategy ◽  
Integrated Or ◽  
Alternative Solutions

<p>Global sensitivity analysis has been recognized as a fundamental tool to assess the input-output model response and evaluate the role of different sources of uncertainty. Among the different methods, variance- and distribution-based (or also called moment-independent) methods have mostly been applied. The first method relies on variance decomposition while the second method compares the entire distributions. The combination of both methods has also been recognized to provide possibly a better assessment. However, the methods rely on different assumptions and the comparison of indices is not straightforward. For these reasons, the methods are commonly not integrated or even considered as alternative solutions. </p><p>In the present contribution, we show a new strategy to combine the two methods in an effective way to perform a comprehensive global sensitivity analysis based on a generic sampling design. The strategy is tested on three commonly-used analytic functions and one hydrological model. The strategy is compared to the state-of-the-art Jansen/Saltelli approach.</p><p>The results show that the new strategy quantifies main effect and interactions consistently. It also outperforms current best practices by converging with a lower number of model runs. For these reasons, the new strategy can be considered as a new and simple approach to perform global sensitivity analysis that can be easily integrated in any environmental models.</p>

scholarly journals A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling

2012 ◽  
Vol 9 (74) ◽  
pp. 2156-2166 ◽  
Author(s):  
T. Sumner ◽  
E. Shephard ◽  
I. D. L. Bogle
Keyword(s):  
Sensitivity Analysis ◽  
Systems Biology ◽  
Computational Models ◽  
Global Sensitivity Analysis ◽  
Insulin Signalling ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Global Sensitivity ◽  
Insulin Signalling Pathway ◽  
Parameter Values

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

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