Standardized maximim D -optimal designs for enzyme kinetic inhibition models

Chen, Ping Yang; Chen, Chih-Chia; Tung, Heng Chin; Wong, Weng Kee

doi:10.1016/j.chemolab.2017.08.009

Cited by 27 publications

(19 citation statements)

References 27 publications

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“…Further, as P.-Y. Chen et al (2017) showed, their speed and ability to provide approximate solutions to the optimum can also lead to an informed conjecture of the global solution that can enable an analytical derivation of the global optimum.…”

Section: Discussionmentioning

confidence: 99%

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Particle swarm optimization for searching efficient experimental designs: A review

Chen

Wong

2022

WIREs Computational Stats

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The class of nature‐inspired metaheuristic algorithms is increasingly used to tackle all kinds of optimization problems across disciplines. It also plays an important component in artificial intelligence and machine learning. Members in this class are general purpose optimization tools that virtually require no assumptions for them to be applicable. There are many such algorithms, and to fix ideas, we review one of its exemplary members called particle swarm optimization (PSO). We discuss the algorithm, its recent applications to find different types of efficient experimental designs, and provide resources, where codes for PSO and other metaheuristic algorithms and tutorials with examples are available. This article is categorized under: Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing

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Section: Discussionmentioning

confidence: 99%

“…In P.-Y Chen et al (2017),. the parameter space of (km, kic) was set as [4, 5] Â [2, 3], and the design space was [15, 30] Â[30, 60].…”

mentioning

confidence: 99%

Particle swarm optimization for searching efficient experimental designs: A review

Chen

Wong

2022

WIREs Computational Stats

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“…They are difficult to find because we have a nested bi-level optimization problem and techniques to search for them are beyond the scope of this paper. Some recent work in constructing minimax or maximin approach using various techniques for different types of design problems are Duarte et al (2018) and Chen et al (2015, 2017).…”

Section: Discussionmentioning

confidence: 99%

A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose

2018

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Hormesis has been widely observed and debated in a variety of context in biomedicine and toxicological sciences. Detecting its presence can be an important problem with wide ranging implications. However, there is little work on constructing an efficient experiment to detect its existence or estimate the threshold dose. We use optimal design theory to develop a variety of locally optimal designs to detect hormesis, estimate the threshold dose and the zero-equivalent point (ZEP) for commonly used models in toxicology and risk assessment. To facilitate use of more efficient designs to detect hormesis, estimate threshold dose and estimate the ZEP in practice, we implement computer algorithms and create a user-friendly web site to help the biomedical researcher generate different types of optimal designs. The online tool facilitates the user to evaluate robustness properties of a selected design to various model assumptions and compare designs before implementation.

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“…Further possibilities regarding the optimization process are robust criteria for design of experiments which are less sensitive to the initial values used for parameter estimation, e.g., the maximin design for optimizing the worst possible performance of any value θ in the parameter space (Körkel et al, 2004; Chen et al, 2017; van Daele et al, 2017; Telen et al, 2018).…”

Section: Michaelis-menten Kinetics As An Example In the Context Of Momentioning

confidence: 99%

Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design

Krausch

Barz

Sawatzki

et al. 2019

Front. Bioeng. Biotechnol.

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Especially in biomanufacturing, methods to design optimal experiments are a valuable technique to fully exploit the potential of the emerging technical possibilities that are driving experimental miniaturization and parallelization. The general objective is to reduce the experimental effort while maximizing the information content of an experiment, speeding up knowledge gain in R&D. The approach of model-based design of experiments (known as MBDoE) utilizes the information of an underlying mathematical model describing the system of interest. A common method to predict the accuracy of the parameter estimates uses the Fisher information matrix to approximate the 90% confidence intervals of the estimates. However, for highly non-linear models, this method might lead to wrong conclusions. In such cases, Monte Carlo sampling gives a more accurate insight into the parameter's estimate probability distribution and should be exploited to assess the reliability of the approximations made through the Fisher information matrix. We first introduce the model-based optimal experimental design for parameter estimation including parameter identification and validation by means of a simple non-linear Michaelis-Menten kinetic and show why Monte Carlo simulations give a more accurate depiction of the parameter uncertainty. Secondly, we propose a very robust and simple method to find optimal experimental designs using Monte Carlo simulations. Although computational expensive, the method is easy to implement and parallelize. This article focuses on practical examples of bioprocess engineering but is generally applicable in other fields.

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Standardized maximim D -optimal designs for enzyme kinetic inhibition models

Cited by 27 publications

References 27 publications

Particle swarm optimization for searching efficient experimental designs: A review

Particle swarm optimization for searching efficient experimental designs: A review

A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose

Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design

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