title: Near-optimal experimental design for model selection in systems biology.
abstract: BACKGROUND
  Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points.
  
  
  RESULTS
  We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation.
  
  
  BACKGROUND
  Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).
authors.affiliation: Department of Computer Science, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Department of Mathematics, ETH Zurich, Department of Biosystems Science and Engineering, ETH Zurich, Swiss Institute of Bioinformatics, Zurich, Switzerland and National ICT Australia, Melbourne, Australia.
authors.forename: Alberto Giovanni
authors.initials: AG
authors.lastname: Busetto
authors.forename: Alain
authors.initials: A
authors.lastname: Hauser
authors.forename: Gabriel
authors.initials: G
authors.lastname: Krummenacher
authors.forename: Mikael
authors.initials: M
authors.lastname: Sunnåker
authors.forename: Sotiris
authors.initials: S
authors.lastname: Dimopoulos
authors.forename: Cheng Soon
authors.initials: CS
authors.lastname: Ong
authors.forename: Jörg
authors.initials: J
authors.lastname: Stelling
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
mesh_terms: D000818:Animals; D008962:Models, Theoretical; D011336:Probability; D012107:Research Design; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology / Q000379:methods*; D058570:TOR Serine-Threonine Kinases / Q000378:metabolism
chemical_list: D058570:TOR Serine-Threonine Kinases
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