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@ARTICLE{Osthege:901977,
      author       = {Osthege, Michael and Tenhaef, Niklas and Zyla, Rebecca and
                      Müller, Carolin and Hemmerich, Johannes and Wiechert,
                      Wolfgang and Noack, Stephan and Oldiges, Marco},
      title        = {bletl - {A} {P}ython {P}ackage for {I}ntegrating
                      {M}icrobioreactors in the {D}esign-{B}uild-{T}est-{L}earn
                      {C}ycle},
      reportid     = {FZJ-2021-03951},
      year         = {2021},
      abstract     = {Microbioreactor (MBR) devices have emerged as powerful
                      cultivation tools for tasks of microbial phenotyping and
                      bioprocess characterization and provide a wealth of online
                      process data in a highly parallelized manner. Such datasets
                      are difficult to interpret in short time by manual
                      workflows. In this study, we present the Python package
                      bletl and show how it enables robust data analyses and the
                      application of machine learning techniques without tedious
                      data parsing and preprocessing. bletl reads raw result files
                      from BioLector I, II and Pro devices to make all the
                      contained information available to Python-based data
                      analysis workflows. Together with standard tooling from the
                      Python scientific computing ecosystem, interactive
                      visualizations and spline-based derivative calculations can
                      be performed. Additionally, we present a new method for
                      unbiased quantification of time-variable specific growth
                      rate based on a novel method of unsupervised switchpoint
                      detection with Student-t distributed random walks. With an
                      adequate calibration model, this method enables
                      practitioners to quantify time-variable growth rate with
                      Bayesian uncertainty quantification and automatically detect
                      switch-points that indicate relevant metabolic changes.
                      Finally, we show how time series feature extraction enables
                      the application of machine learning methods to MBR data,
                      resulting in unsupervised phenotype characterization. As an
                      example, t-distributed Stochastic Neighbor Embedding (t-SNE)
                      is performed to visualize datasets comprising a variety of
                      growth/DO/pH phenotypes.},
      cin          = {IBG-1},
      cid          = {I:(DE-Juel1)IBG-1-20101118},
      pnm          = {2172 - Utilization of renewable carbon and energy sources
                      and engineering of ecosystem functions (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2172},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2021.08.24.457462},
      url          = {https://juser.fz-juelich.de/record/901977},
}