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@ARTICLE{Reiter:906798,
      author       = {Reiter, Alexander and Asgari, Jian and Wiechert, Wolfgang
                      and Oldiges, Marco},
      title        = {{M}etabolic {F}ootprinting of {M}icrobial {S}ystems {B}ased
                      on {C}omprehensive {I}n {S}ilico {P}redictions of {MS}/{MS}
                      {R}elevant {D}ata},
      journal      = {Metabolites},
      volume       = {12},
      number       = {3},
      issn         = {2218-1989},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-01699},
      pages        = {257 -},
      year         = {2022},
      abstract     = {Metabolic footprinting represents a holistic approach to
                      gathering large-scale metabolomic information of a given
                      biological system and is, therefore, a driving force for
                      systems biology and bioprocess development. The ongoing
                      development of automated cultivation platforms increases the
                      need for a comprehensive and rapid profiling tool to cope
                      with the cultivation throughput. In this study, we
                      implemented a workflow to provide and select relevant
                      metabolite information from a genome-scale model to
                      automatically build an organism-specific comprehensive
                      metabolome analysis method. Based on in-house literature and
                      predicted metabolite information, the deduced metabolite set
                      was distributed in stackable methods for a
                      chromatography-free dilute and shoot flow-injection analysis
                      multiple-reaction monitoring profiling approach. The
                      workflow was used to create a method specific for
                      Saccharomyces cerevisiae, covering 252 metabolites with 7
                      min/sample. The method was validated with a commercially
                      available yeast metabolome standard, identifying up to
                      $74.2\%$ of the listed metabolites. As a first case study,
                      three commercially available yeast extracts were screened
                      with 118 metabolites passing quality control thresholds for
                      statistical analysis, allowing to identify discriminating
                      metabolites. The presented methodology provides metabolite
                      screening in a time-optimised way by scaling analysis time
                      to metabolite coverage and is open to other microbial
                      systems simply starting from genome-scale model
                      information.},
      cin          = {IBG-1},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IBG-1-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35323700},
      UT           = {WOS:000774162600001},
      doi          = {10.3390/metabo12030257},
      url          = {https://juser.fz-juelich.de/record/906798},
}