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@ARTICLE{WeusterBotz:34340,
      author       = {Weuster-Botz, D.},
      title        = {{E}xperimental design for fermentation media development :
                      statistical design or global random search?},
      journal      = {Journal of bioscience and bioengineering},
      volume       = {90},
      issn         = {1389-1723},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-34340},
      pages        = {473 - 483},
      year         = {2000},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {The diversity of combinatorial interactions of medium
                      components with the metabolism of cells as wed as the large
                      number of medium constituents necessary for cellular growth
                      and production do not permit satisfactory detailed
                      modelling. For this reason, experimental search procedures
                      in simultaneous shaking flask experiments are used to
                      optimise fermentation media. As an alternative to the
                      methods of statistical experimental design employed in this
                      field for many decades, the use of stochastic search
                      procedures has been evaluated recently, since these require
                      neither the unimodality of the response surface nor
                      limitations in the number of medium components under
                      consideration. Genetic algorithms were selected due to their
                      basic capability for efficient exploration of large variable
                      spaces. Using a genetic algorithm, it has been
                      experimentally verified, with the aid of process examples,
                      that process improvements can be achieved both for microbial
                      and enzymatic conversions and for cell cultures despite the
                      large number of medium components under simultaneous
                      consideration (about 10 or more). In exploring a new
                      variable space, process improvements of more than $100\%$
                      were generally achieved. For initial reaction conditions
                      previously 'optimised' via standard procedures it has been
                      possible in most cases to achieve a further improvement of
                      $20-40\%$ of the target quantity. Although the genetic
                      algorithm can be very efficient for exploration of large
                      variable spaces, it is improbable that a 'global optimum'
                      can be precisely identified because of the relatively small
                      number of shaking flask experiments usually performed. As a
                      consequence, a combination of highly directed random
                      searches to explore the n-dimensional variable space with
                      the genetic algorithm and subsequent application of
                      classical statistical experimental design is recommended for
                      media development.},
      keywords     = {J (WoSType)},
      cin          = {IBT-2},
      ddc          = {570},
      cid          = {I:(DE-Juel1)VDB56},
      pnm          = {Verfahrenstechnik zur mikrobiellen Gewinnung von
                      Primärmetaboliten},
      pid          = {G:(DE-Juel1)FUEK93},
      shelfmark    = {Biotechnology $\&$ Applied Microbiology / Food Science $\&$
                      Technology},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000165594800001},
      doi          = {10.1263/jbb.90.473},
      url          = {https://juser.fz-juelich.de/record/34340},
}