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

DOI10.1002/rnc.2797zbMath1258.93112OpenAlexW1750536449WikidataQ59343559 ScholiaQ59343559MaRDI QIDQ4909331

María Rodríguez-Fernández, Julio R. Banga, Francis J. III. Doyle

Publication date: 13 March 2013

Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10261/49061

zbMATH Keywords

robustnessparameter estimationglobal sensitivity analysissystems biologymodel identificationparameter distribution

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12) Control/observation systems governed by ordinary differential equations (93C15) Systems biology, networks (92C42)

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Uses Software

• ODESSA
• sbtoolbox
• COPASI
• SensSB
• SBML-SAT
• PottersWheel

Cites Work

• Unnamed Item
• Scatter search for chemical and bio-process optimization
• R. A. Fisher and the making of maximum likelihood 1912--1922
• Making best use of model evaluations to compute sensitivity indices
• Numerical methods and software for sensitivity analysis of differential-algebraic systems
• Derivative based global sensitivity measures and their link with global sensitivity indices
• Algorithm 658
• Sensitivity Analysis in Practice
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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