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@ARTICLE{Helleckes:906761,
      author       = {Helleckes, Laura Marie and Osthege, Michael and Wiechert,
                      Wolfgang and von Lieres, Eric and Oldiges, Marco},
      title        = {{B}ayesian calibration, process modeling and uncertainty
                      quantification in biotechnology},
      journal      = {PLoS Computational Biology},
      volume       = {18},
      number       = {3},
      issn         = {1553-734X},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2022-01675},
      pages        = {e1009223 -},
      year         = {2022},
      abstract     = {High-throughput experimentation has revolutionized
                      data-driven experimental sciences and opened the door to the
                      application of machine learning techniques. Nevertheless,
                      the quality of any data analysis strongly depends on the
                      quality of the data and specifically the degree to which
                      random effects in the experimental data-generating process
                      are quantified and accounted for. Accordingly calibration,
                      i.e. the quantitative association between observed
                      quantities and measurement responses, is a core element of
                      many workflows in experimental sciences.Particularly in life
                      sciences, univariate calibration, often involving non-linear
                      saturation effects, must be performed to extract
                      quantitative information from measured data. At the same
                      time, the estimation of uncertainty is inseparably connected
                      to quantitative experimentation. Adequate calibration models
                      that describe not only the input/output relationship in a
                      measurement system but also its inherent measurement noise
                      are required. Due to its mathematical nature, statistically
                      robust calibration modeling remains a challenge for many
                      practitioners, at the same time being extremely beneficial
                      for machine learning applications.In this work, we present a
                      bottom-up conceptual and computational approach that solves
                      many problems of understanding and implementing non-linear,
                      empirical calibration modeling for quantification of
                      analytes and process modeling. The methodology is first
                      applied to the optical measurement of biomass concentrations
                      in a high-throughput cultivation system, then to the
                      quantification of glucose by an automated enzymatic assay.
                      We implemented the conceptual framework in two Python
                      packages, calibr8 and murefi, with which we demonstrate how
                      to make uncertainty quantification for various calibration
                      tasks more accessible. Our software packages enable more
                      reproducible and automatable data analysis routines compared
                      to commonly observed workflows in life
                      sciences.Subsequently, we combine the previously established
                      calibration models with a hierarchical Monod-like ordinary
                      differential equation model of microbial growth to describe
                      multiple replicates of Corynebacterium glutamicum batch
                      cultures. Key process model parameters are learned by both
                      maximum likelihood estimation and Bayesian inference,
                      highlighting the flexibility of the statistical and
                      computational framework.},
      cin          = {IBG-1},
      ddc          = {610},
      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)16},
      pubmed       = {35255090},
      UT           = {WOS:001084616000001},
      doi          = {10.1371/journal.pcbi.1009223},
      url          = {https://juser.fz-juelich.de/record/906761},
}