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@ARTICLE{Haunschild:53099,
      author       = {Haunschild, M. D. and Wahl, S. A. and Freisleben, B. and
                      Wiechert, W.},
      title        = {{A} general framework for large scale model selection},
      journal      = {Optimization methods $\&$ software},
      volume       = {21},
      issn         = {1055-6788},
      address      = {London [u.a.]},
      publisher    = {Taylor $\&$ Francis},
      reportid     = {PreJuSER-53099},
      pages        = {901 - 917},
      year         = {2006},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {Model selection is concerned with the choice of a
                      mathematical model from a set of candidates that best
                      describes a given set of experimental data. Large families
                      of models arise in the context of structured mechanistic
                      modelling in several application fields. In this situation
                      the model selection problem cannot be solved by brute force
                      testing of all possible models because of the high
                      computational costs. However, more information on the
                      different models of a family is available by their
                      interdependencies, given by generalization or simplification
                      relations. Large-scale model selection algorithms should
                      exploit these relations for navigation in the discrete space
                      of all model candidates. This paper presents a general
                      approach for large-scale model selection by specifying the
                      necessary computational primitives for navigating in large
                      model families. As a non-trivial example it is shown how
                      families of biochemical network models arising from the
                      evaluation of stimulus response experiments are mapped to
                      the general formalism. Finally, a first model selection
                      algorithm based on the mentioned computational primitives is
                      introduced and applied to complex biochemical network
                      experiments. It is based on a load-balancing algorithm by
                      making use of grid computing facilities.},
      keywords     = {J (WoSType)},
      cin          = {IBT-2},
      ddc          = {510},
      cid          = {I:(DE-Juel1)VDB56},
      pnm          = {Biotechnologie},
      pid          = {G:(DE-Juel1)FUEK410},
      shelfmark    = {Computer Science, Software Engineering / Operations
                      Research $\&$ Management Science / Mathematics, Applied},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000240592700004},
      doi          = {10.1080/10556780600872208},
      url          = {https://juser.fz-juelich.de/record/53099},
}