Optimal Experimental Design for Discriminating Numerous Model Candidates: The AWDC Criterion

Michalik, Claas; Stuckert, Maxim; Marquardt, Wolfgang

doi:10.1021/ie900903u

Cited by 31 publications

(47 citation statements)

References 39 publications

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“…Even though a global maximum can be reached at these conditions, this so-called model lumping phenomenon disables the discrimination between models within the group. Recently, Michalik et al 15 developed a methodology based on the Akaike's information criterion. The proposed Akaike Weights Design Criterion (AWDC) was shown to prevent model lumping successfully and to identify the best model structure very efficiently, even if numerous rival models were considered.…”

Section: Introductionmentioning

confidence: 99%

Experimental Design for Discrimination of Chemical Kinetic Models for Oxy-Methane Combustion

et al. 2017

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The concept of combustion under oxy-fuel conditions has the potential to reduce greenhouse gas emissions. For the design of combustion devices operating under these conditions, a good understanding of fuel oxidation behavior in terms of chemical kinetic mechanisms is useful. For the oxidation of the main component of natural gas and coal devolatilization products, i. e. methane, various chemical mechanisms are available in the literature validated mostly with experiments using air, and none of them is developed particularly or has been validated extensively for oxy-methane combustion. An important prerequisite for model assessment is high-quality data typically obtained from resource-and time-consuming measurements. The aim of this study is to identify the best methane mechanism for oxy-fuel combustion from a set of models available in the literature with a minimum number of measurements. Five chemical models, which have been validated for the oxidation of methane/air mixtures, are compared in terms of their performance for extinction strain rates, ignition delay times, and laminar burning velocities of oxy-methane mixtures. A model-based experimental design method, i. e. Akaike Weights Design Criterion, is applied to determine the optimal potential measurements. Ideally, at the conditions of designed experiments, model predictions are nicely separated and thus the best model can be identified by comparison with these measurements. It is shown that the employed experimental design strategy identifies informative experiments for model discrimination efficiently. While measurements of extinction strain rates are proposed to be carried out for flames with small methane mass fractions of the fuel stream and oxygen mass fractions of the oxidizer stream, shock tube experiments are evaluated as equally useful for model discrimination over the investigated range of conditions. Measurements of flame speeds are designed at very small and very large equivalence ratios particularly at relatively high pressures.

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Section: Introductionmentioning

confidence: 99%

Experimental Design for Discrimination of Chemical Kinetic Models for Oxy-Methane Combustion

et al. 2017

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“…For chemical reaction kinetics and biotechnology, there exists some work on designing dynamic stimuli for the purpose of model discrimination (Asprey and Macchietto, 2002; Box and Hill, 1967; Kremling et al , 2004). As has been illustrated, the consideration of model response variabilities strongly improves the designed experiments and experimental data quality (Chen and Asprey, 2003; Donckels, 2012; Michalik et al , 2010). Therefore, linearization of the system’s parameter mapping has been used.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks

Flassig¹,

Sundmacher²

2012

Bioinformatics

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Motivation: Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs).Results: In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning.Availability: An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html.Contact: flassig@mpi-magdeburg.mpg.deSupplementary information: Supplementary data are are available at Bioinformatics online.

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“…Model-based OED strategies that aim to maximize parameter precision are regularly applied in industry and academia [19]. In systems biology, only a few authors give attention to application of OED techniques (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques are tailored to few models and their use is desirable in an early model identification phase. At this stage there is limited information on the expected outcomes from the data measurements [19]. Despite these draw-backs, OEDs are still very popular for extraction of useful information from data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of design strategies for time course experiments in genetic networks

Omony

Mach-Aigner

Graaff

et al. 2011

Proceedings of the 9th International Conference on Computational Methods in Systems Biology

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One of the challenges in the reconstruction of genetic network is to find experimental designs that maximize the information content in the data. In this work the information value of time course experiments (TCEs) is used to rank experimental designs. The study concerns the dynamic response of genes in the XlnR regulon of Aspergillus niger, whereby it was the goal to find the best moment to administer an extra pulse of inducing D-xylose. Low and high trigger concentrations are considered. The models that govern the regulation of the target genes in this regulon are used for simulation. Parameter sensitivity analysis, Fisher Information Matrix (FIM) and the E-modified criterion are used for the design performance assessment. The results show that * Corresponding author. Performed the modeling, network identification and wrote the manuscript.† Provided biological knowledge for the modeling. ‡ Provided biological knowledge for the modeling. § Contributed in calculations and critical review of the methods used. ¶ Contributed in calculations and critical review of the methods used.

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Optimal Experimental Design for Discriminating Numerous Model Candidates: The AWDC Criterion

Cited by 31 publications

References 39 publications

Experimental Design for Discrimination of Chemical Kinetic Models for Oxy-Methane Combustion

Experimental Design for Discrimination of Chemical Kinetic Models for Oxy-Methane Combustion

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks

Evaluation of design strategies for time course experiments in genetic networks

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