% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Hemmerich:889214,
      author       = {Hemmerich, Johannes and Tenhaef, Niklas and Wiechert,
                      Wolfgang and Noack, Stephan},
      title        = {py{FOOMB}: {P}ython framework for object oriented modeling
                      of bioprocesses},
      journal      = {Engineering in life sciences},
      volume       = {21},
      number       = {3-4},
      issn         = {1618-2863},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {FZJ-2021-00120},
      pages        = {242-257},
      year         = {2021},
      abstract     = {Quantitative characterization of biotechnological
                      production processes requires the determination of different
                      key performance indicators (KPIs) such as titer, rate and
                      yield. Classically, these KPIs can be derived by combining
                      black‐box bioprocess modeling with non‐linear regression
                      for model parameter estimation. The presented pyFOOMB
                      package enables a guided and flexible implementation of
                      bioprocess models in the form of ordinary differential
                      equation systems (ODEs). By building on Python as powerful
                      and multi‐purpose programing language, ODEs can be
                      formulated in an object‐oriented manner, which facilitates
                      their modular design, reusability, and extensibility. Once
                      the model is implemented, seamless integration and analysis
                      of the experimental data is supported by various Python
                      packages that are already available. In particular, for the
                      iterative workflow of experimental data generation and
                      subsequent model parameter estimation we employed the
                      concept of replicate model instances, which are linked by
                      common sets of parameters with global or local properties.
                      For the description of multi‐stage processes,
                      discontinuities in the right‐hand sides of the
                      differential equations are supported via event handling
                      using the freely available assimulo package. Optimization
                      problems can be solved by making use of a parallelized
                      version of the generalized island approach provided by the
                      pygmo package. Furthermore, pyFOOMB in combination with
                      Jupyter notebooks also supports education in bioprocess
                      engineering and the applied learning of Python as scientific
                      programing language. Finally, the applicability and
                      strengths of pyFOOMB will be demonstrated by a comprehensive
                      collection of notebook examples.},
      cin          = {IBG-1},
      ddc          = {660},
      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},
      UT           = {WOS:000605178700001},
      doi          = {10.1002/elsc.202000088},
      url          = {https://juser.fz-juelich.de/record/889214},
}