Literature DB >> 29332979

Standardized maximim D-optimal designs for enzyme kinetic inhibition models.

Ping-Yang Chen1, Ray-Bing Chen1, Heng-Chin Tung1, Weng Kee Wong2.   

Abstract

Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters. An alternative is the maximin approach that allows the user to specify a range of values for each parameter of interest. However, the maximin approach is difficult because we first have to determine the locally optimal design for each set of nominal values before maximin types of optimal designs can be found via a nested optimization process. We show that particle swarm optimization (PSO) techniques can solve such complex optimization problems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae for standardized maximin D-optimal designs for nonlinear model with 3 or 4 parameters on the compact and nonnegative design space. Additionally, we show locally and standardized maximin D-optimal designs for inhibition models are not necessarily supported at a minimum number of points. To facilitate use of such designs, we create a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs.

Entities:  

Keywords:  Approximate design; Locally D-optimal design; Nonlinear model; Particle swarm optimization

Year:  2017        PMID: 29332979      PMCID: PMC5761082          DOI: 10.1016/j.chemolab.2017.08.009

Source DB:  PubMed          Journal:  Chemometr Intell Lab Syst        ISSN: 0169-7439            Impact factor:   3.491


  8 in total

1.  Minimax D-optimal designs for the logistic model.

Authors:  J King; W K Wong
Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

2.  Design issues for the Michaelis-Menten model.

Authors:  J López-Fidalgo; Weng Kee Wong
Journal:  J Theor Biol       Date:  2002-03-07       Impact factor: 2.691

3.  Bioassay case study applying the maximin D-optimal design algorithm to the four-parameter logistic model.

Authors:  Todd Coffey
Journal:  Pharm Stat       Date:  2015-07-31       Impact factor: 1.894

4.  Optimum design of experiments for enzyme inhibition kinetic models.

Authors:  Barbara Bogacka; Maciej Patan; Patrick J Johnson; Kuresh Youdim; Anthony C Atkinson
Journal:  J Biopharm Stat       Date:  2011-05       Impact factor: 1.051

5.  A novel global search algorithm for nonlinear mixed-effects models using particle swarm optimization.

Authors:  Seongho Kim; Lang Li
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-06-30       Impact factor: 2.745

6.  'Optimal' designs for drug, neurotransmitter and hormone receptor assays.

Authors:  G Dunn
Journal:  Stat Med       Date:  1988-07       Impact factor: 2.373

7.  Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.

Authors:  Frederick Kin Hing Phoa; Ray-Bing Chen; Weichung Wang; Weng Kee Wong
Journal:  Technometrics       Date:  2016-01-22

8.  Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

Authors:  Jiaheng Qiu; Ray-Bing Chen; Weichung Wang; Weng Kee Wong
Journal:  Swarm Evol Comput       Date:  2014-10-01       Impact factor: 7.177

  8 in total
  3 in total

1.  Using Differential Evolution to Design Optimal Experiments.

Authors:  Zack Stokes; Abhyuday Mandal; Weng Kee Wong
Journal:  Chemometr Intell Lab Syst       Date:  2020-01-28       Impact factor: 3.491

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

Authors:  Niels Krausch; Tilman Barz; Annina Sawatzki; Mathis Gruber; Sarah Kamel; Peter Neubauer; Mariano Nicolas Cruz Bournazou
Journal:  Front Bioeng Biotechnol       Date:  2019-05-24

3.  G-optimal designs for hierarchical linear models: an equivalence theorem and a nature-inspired meta-heuristic algorithm.

Authors:  Xin Liu; RongXian Yue; Zizhao Zhang; Weng Kee Wong
Journal:  Soft comput       Date:  2021-08-07       Impact factor: 3.732

  3 in total

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