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@ARTICLE{Helleckes:894764,
      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},
      reportid     = {FZJ-2021-03378},
      year         = {2021},
      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 with 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, with which we demonstrate
                      how it makes 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 differential equation model of microbial growth
                      to describe multiple replicates of Corynebacterium
                      glutamicum batch microbioreactor 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},
      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)25},
      doi          = {10.1101/2021.06.30.450546},
      url          = {https://juser.fz-juelich.de/record/894764},
}