Sensitivity analysis for the reduction of complex metabolism models

Degenring, Daniela; Froemel, C.; Dikta, Gerhard; Takors, Ralf

doi:10.1016/j.jprocont.2003.12.008

Cited by 110 publications

(88 citation statements)

References 24 publications

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“…Under standard assumptions, parameter estimation is obtained by solving the following optimization 3 IκBα n , x 16 IκBβ, x 4 IκBα n -NF-κB n , X 17 IκBβ-NF-κB, x 5 IκBβ n , x 18 IκBε, x 6 IκBβ n -NF-κB n , x 19 IκBε-NF-κB, x 7 IκBε n , x 20 IKKIκBα, x 8 IκBε n -NF-κB n , x 21 IKKIκBα-NF-κB, x 9 Source (S = 1) IKK, x 10 IκBα −t , x 22 IKKIκBβ, x 11 Sink (sink = 0) IKKIκBβ-NF-κB, x 12 IκBβ −t , x 23 IKKIκBε, x 13 IκBε −t , x 24 problem:θ…”

Section: Local Sensitivity Analysis and Optimal Experimental Designmentioning

confidence: 99%

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Sensitivity analysis and robust experimental design of a signal transduction pathway system

Yue

Brown

et al. 2008

Int J of Chemical Kinetics

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Experimental design for cellular networks based on sensitivity analysis is studied in this work. Both optimal and robust experimental design strategies are developed for the IκB-NF-κB signal transduction model. Based on local sensitivity analysis, the initial IKK intensity is calculated using an optimal experimental design process, and several scalarization measures of the Fisher information matrix are compared. Global sensitivity analysis and robust experimental design techniques are then developed to consider parametric uncertainties in the model. The modified Morris method is employed in global sensitivity analysis, and a semidefinite programming method is exploited to implement the robust experimental design for the problem of measurement set selection. The parametric impacts on the oscillatory behavior of NF-κB in the nucleus are also discussed.

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Section: Local Sensitivity Analysis and Optimal Experimental Designmentioning

confidence: 99%

“…It is widely used in modeling and analysis of biological systems, in which the nominal parameter values are estimated using experimental data or computation [4][5][6][7]. For continuous dynamic systems, the local sensitivities are defined as the first-order partial derivatives of the system output with respect to the input parameters.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and robust experimental design of a signal transduction pathway system

Yue

Brown

et al. 2008

Int J of Chemical Kinetics

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“…6 In a recent work on model reduction of complex metabolism models, time-varying local sensitivity analysis has been performed to compose the matrix of normalized sensitivity coefficients, based on which, different methods were used to discard parameters that have less influence on the model dynamics. 7 Using the Monte Carlo method, Cho et al employed multi-parametric global sensitivity analysis on the TNFa-mediated NF-kB signal transduction pathway for experimental design. 8 Schwacke and Voit presented a Taylor integration method for the efficient computation of timedependent sensitivities for generalized mass action systems, then investigated the effects of different initial species concentrations on the system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-κB signalling pathway

et al. 2006

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This version is available at https://strathprints.strath.ac.uk/5175/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kB signalling pathway{ Mathematical modelling offers a variety of useful techniques to help in understanding the intrinsic behaviour of complex signal transduction networks. From the system engineering point of view, the dynamics of metabolic and signal transduction models can always be described by nonlinear ordinary differential equations (ODEs) following mass balance principles. Based on the statespace formulation, many methods from the area of automatic control can conveniently be applied to the modelling, analysis and design of cell networks. In the present study, dynamic sensitivity analysis is performed on a model of the IkB-NF-kB signal pathway system. Univariate analysis of the Euclidean-form overall sensitivities shows that only 8 out of the 64 parameters in the model have major influence on the nuclear NF-kB oscillations. The sensitivity matrix is then used to address correlation analysis, identifiability assessment and measurement set selection within the framework of least squares estimation and multivariate analysis. It is shown that certain pairs of parameters are exactly or highly correlated to each other in terms of their effects on the measured variables. The experimental design strategy provides guidance on which proteins should best be considered for measurement such that the unknown parameters can be estimated with the best statistical precision. The whole analysis scheme we describe provides efficient parameter estimation techniques for complex cell networks.

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“…Due to their poor accuracy, the estimation of these parameters can lead to significant degradation in the predictive capability of the model. Different selection methods based on the estimability principle have been developed as reported in the literature, including the principal component method (Degenring et al (2004), Turanyi (1990)), the singular value decomposition (Velez-Reyes and Verghese (1995)), the correlation methods (Jacquez and Greif (1985)) and the eigenvalue method (Vajda et al (1989)). A good review of the most important methods can be found in Quaiser and Mönnigmann (2009) and McLean et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Emulsion copolymerization of styrene and butyl acrylate in the presence of a chain transfer agent. Part 2: Parameters estimability and confidence regions

Benyahia¹,

Latifi²,

Fonteix³

et al. 2013

Chemical Engineering Science

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Accurate estimation of the model parameters is required to obtain reliable predictions of the products end-use properties. However, due to the mathematical model structure and/or to a possible lack of measurements, the estimation of some parameters may be impossible. This paper will focus on the case where the main limitations to the parameters estimability are their weak effect on the measured outputs or the correlation between the effects of two or more parameters. The objective of the method developed in this paper is to determine the subset of the most influencing parameters that can be estimated from the available experimental data, when the complete set of model parameters cannot be estimated. This approach has been applied to the mathematical model of the emulsion copolymerization of styrene and butyl acrylate, in the presence of n-dodecyl mercaptan as a chain transfer agent. In addition, a new approach is used to better assess the true confidence regions and evaluate the accuracy of the parameters estimates in more reliable way.

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Sensitivity analysis for the reduction of complex metabolism models

Cited by 110 publications

References 24 publications

Sensitivity analysis and robust experimental design of a signal transduction pathway system

Sensitivity analysis and robust experimental design of a signal transduction pathway system

Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-κB signalling pathway

Emulsion copolymerization of styrene and butyl acrylate in the presence of a chain transfer agent. Part 2: Parameters estimability and confidence regions

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