Optimal experiment design

Emery, A. F.; Ненарокомов, А. В.

doi:10.1088/0957-0233/9/6/003

Cited by 143 publications

(142 citation statements)

References 19 publications

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“…The tasks of experimental design include input signal design, sampling rate optimization, measurement set selection, and so on. Under the assumption of uncorrelated measurement noise with zero-mean Gaussian distribution, the information content of measurements can be quantified by the Fisher information matrix (FIM) [17,18]. In general, the smaller the joint confidence interval is for the estimated parameters, the more information is contained in the measurements.…”

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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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: 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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“…By the CramerRao theorem [29], the covariance matrix of any unbiased estimatorθ is lower bounded by the Cramer-Rao Lower Bound (CRLB), or the inverse of the Fisher Information Matrix FIM(θ ⋆ , Q):…”

Section: Optimal Experiments Designmentioning

confidence: 99%

“…Therefore, the problem is to find a correct configuration of M measurement points, Q := {q m } M m=1 such that the size of the confidence region of the parameter estimates is minimized. This is achieved by maximizing the determinant of the FIM (D-optimality [29]):…”

Section: Optimal Experiments Designmentioning

confidence: 99%

Evaluation of three inverse problem models to quantify skin microcirculation using diffusion-weighted MRI

Cordier

Choi

Raguin

2008

J. Phys.: Conf. Ser.

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Abstract. Skin microcirculation plays an important role in diseases such as chronic venous insufficiency and diabetes. Magnetic resonance imaging (MRI) can provide quantitative information with a better penetration depth than other noninvasive methods, such as laser Doppler flowmetry or optical coherence tomography. Moreover, successful MRI skin studies have recently been reported. In this article, we investigate three potential inverse models to quantify skin microcirculation using diffusion-weighted MRI (DWI), also known as q-space MRI. The model parameters are estimated based on nonlinear least-squares (NLS). For each of the three models, an optimal DWI sampling scheme is proposed based on D-optimality in order to minimize the size of the confidence region of the NLS estimates and thus the effect of the experimental noise inherent to DWI. The resulting covariance matrices of the NLS estimates are predicted by asymptotic normality and compared to the ones computed by Monte-Carlo simulations. Our numerical results demonstrate the effectiveness of the proposed models and corresponding DWI sampling schemes as compared to conventional approaches.

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“…For the purpose of parameter estimation, experiments are generally designed to generate the richest information. In this regard, the FIM whose inverse provides an estimate of the lower bound of parameter variance-covariance [7], has been commonly used to quantify data informativeness [8][9][10][11][12][13][14]. In the past decade, FIM-based MBDOE methods have newfound applications in emerging areas such as systems biology [11,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix

Manesso¹,

Sridharan²

2017

Preprint

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Abstract:The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher Information Matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs since the FIM only accounts for the linear variation of the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, where model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model curvature. We demonstrated the advantages of the MOO MBDOE over existing FIM-based and other curvature-based MBDOEs in an application to the kinetic modeling of fed-batch fermentation of Baker's yeast.

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Optimal experiment design

Cited by 143 publications

References 19 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

Evaluation of three inverse problem models to quantify skin microcirculation using diffusion-weighted MRI

Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix

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