title: Trajectory-oriented Bayesian experiment design versus Fisher A-optimal design: an in depth comparison study.
abstract: BACKGROUND
  Experiment design strategies for biomedical models with the purpose of parameter estimation or model discrimination are in the focus of intense research. Experimental limitations such as sparse and noisy data result in unidentifiable parameters and render-related design tasks challenging problems. Often, the temporal resolution of data is a limiting factor and the amount of possible experimental interventions is finite. To address this issue, we propose a Bayesian experiment design algorithm to minimize the prediction uncertainty for a given set of experiments and compare it to traditional A-optimal design.
  
  
  RESULTS
  In an in depth numerical study involving an ordinary differential equation model of the trans-Golgi network with 12 partly non-identifiable parameters, we minimized the prediction uncertainty efficiently for predefined scenarios. The introduced method results in twice the prediction precision as the same amount of A-optimal designed experiments while introducing a useful stopping criterion. The simulation intensity of the algorithm's major design step is thereby reasonably affordable. Besides smaller variances in the predicted trajectories compared with Fisher design, we could also achieve smaller parameter posterior distribution entropies, rendering this method superior to A-optimal Fisher design also in the parameter space.
  
  
  BACKGROUND
  Necessary software/toolbox information are available in the supplementary material. The project script including example data can be downloaded from http://www.ist.uni-stuttgart.de/%7eweber/BayesFisher2012.
  
  
  BACKGROUND
  patrick.weber@ist.uni-stuttgart.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, Stuttgart 70550, Germany. patrick.weber@ist.uni-stuttgart.de
authors.forename: Patrick
authors.initials: P
authors.lastname: Weber
authors.forename: Andrei
authors.initials: A
authors.lastname: Kramer
authors.forename: Clemens
authors.initials: C
authors.lastname: Dingler
authors.forename: Nicole
authors.initials: N
authors.lastname: Radde
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D008954:Models, Biological; D012107:Research Design; D055571:Secretory Pathway; D035501:Uncertainty; D021601:trans-Golgi Network / Q000378:metabolism