---
_id: '13652'
abstract:
- lang: eng
  text: "Modellbasierte Konzepte und Simulationstechniken in Kombination mit digitalen
    Werkzeugen erweisen sich als Schlüssel, um das volle Potenzial biopharmazeutischer
    Produktionsprozesse zu erschließen, die mehrere herausfordernde Entwicklungs-
    und Prozessschritte enthalten. Einer dieser Schritte ist der zeit- und kostenintensive
    Zellproliferationsprozess (auch als Seed Train bezeichnet), um die Zellzahl vom
    Auftauen der Zellen bis zum Produktionsmaßstab zu erhöhen. Herausforderungen wie
    komplexer Zellstoffwechsel, Chargen-zu-Chargen-Variationen, Variabilitäten im
    Zellverhalten und Einflüsse von Änderungen der Kultivierungsbedingungen erfordern
    adäquate digitale Lösungen, um Informationen über den aktuellen und zukünftigen
    Prozesszustand bereitzustellen und korrekte Prozessentscheidungen abzuleiten.\r\n\r\nZu
    diesem Zweck haben sich digitale Seed Train Zwillinge als effizient erwiesen,
    die das zeitabhängige Verhalten wichtiger Prozessvariablen basierend auf mathematischen
    Modellen, Strategien und Anpassungsverfahren digital darstellen.\r\n\r\nDieses
    Kapitel skizziert die Notwendigkeit der Digitalisierung von Seed Trains, den Aufbau
    eines digitalen Seed Train Zwillings, die Rolle der Parameterschätzung und verschiedene
    statistische Methoden in diesem Zusammenhang, die auf mehrere Probleme im Bereich
    der Bioprozessierung anwendbar sind. Die Ergebnisse einer Fallstudie werden vorgestellt,
    um einen Bayes’schen Ansatz zur Parameterschätzung und Vorhersage eines industriellen
    Zellkultur-Seed Trains für die Seed Train Digitalisierung zu veranschaulichen."
author:
- first_name: Tanja
  full_name: Hernández Rodriguez, Tanja
  id: '52466'
  last_name: Hernández Rodriguez
- first_name: Björn
  full_name: Frahm, Björn
  id: '45666'
  last_name: Frahm
citation:
  ama: 'Hernández Rodriguez T, Frahm B. Digitale Seed Train Zwillinge und statistische
    Methoden. In: Herwig C, Pörtner R, Möller J, eds. <i>Digitale Zwillinge - Werkzeuge
    und Konzepte für intelligente Bioproduktion</i>. Advances in Biochemical Engineering/Biotechnology.
    Springer ; 2025:107-145. doi:<a href="https://doi.org/10.1007/978-3-031-75698-6_5">https://doi.org/10.1007/978-3-031-75698-6_5</a>'
  apa: Hernández Rodriguez, T., &#38; Frahm, B. (2025). Digitale Seed Train Zwillinge
    und statistische Methoden. In C. Herwig, R. Pörtner, &#38; J. Möller (Eds.), <i>Digitale
    Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i> (pp. 107–145).
    Springer . <a href="https://doi.org/10.1007/978-3-031-75698-6_5">https://doi.org/10.1007/978-3-031-75698-6_5</a>
  bjps: '<b>Hernández Rodriguez T and Frahm B</b> (2025) Digitale Seed Train Zwillinge
    und statistische Methoden. In Herwig C, Pörtner R and Möller J (eds), <i>Digitale
    Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>. Berlin,
    Heidelberg: Springer , pp. 107–145.'
  chicago: 'Hernández Rodriguez, Tanja, and Björn Frahm. “Digitale Seed Train Zwillinge
    und statistische Methoden.” In <i>Digitale Zwillinge - Werkzeuge und Konzepte
    für intelligente Bioproduktion</i>, edited by Christoph Herwig, Ralf Pörtner,
    and Johannes Möller, 107–45. Advances in Biochemical Engineering/Biotechnology.
    Berlin, Heidelberg: Springer , 2025. <a href="https://doi.org/10.1007/978-3-031-75698-6_5">https://doi.org/10.1007/978-3-031-75698-6_5</a>.'
  chicago-de: 'Hernández Rodriguez, Tanja und Björn Frahm. 2025. Digitale Seed Train
    Zwillinge und statistische Methoden. In: <i>Digitale Zwillinge - Werkzeuge und
    Konzepte für intelligente Bioproduktion</i>, hg. von Christoph Herwig, Ralf Pörtner,
    und Johannes Möller, 107–145. Advances in Biochemical Engineering/Biotechnology.
    Berlin, Heidelberg: Springer . doi:<a href="https://doi.org/10.1007/978-3-031-75698-6_5">https://doi.org/10.1007/978-3-031-75698-6_5</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Hernández Rodriguez, Tanja</span>
    ; <span style="font-variant:small-caps;">Frahm, Björn</span>: Digitale Seed Train
    Zwillinge und statistische Methoden. In: <span style="font-variant:small-caps;">Herwig,
    C.</span> ; <span style="font-variant:small-caps;">Pörtner, R.</span> ; <span
    style="font-variant:small-caps;">Möller, J.</span> (Hrsg.): <i>Digitale Zwillinge
    - Werkzeuge und Konzepte für intelligente Bioproduktion</i>, <i>Advances in Biochemical
    Engineering/Biotechnology</i>. Berlin, Heidelberg : Springer , 2025, S. 107–145'
  havard: 'T. Hernández Rodriguez, B. Frahm, Digitale Seed Train Zwillinge und statistische
    Methoden, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digitale Zwillinge - Werkzeuge
    und Konzepte für intelligente Bioproduktion, Springer , Berlin, Heidelberg, 2025:
    pp. 107–145.'
  ieee: 'T. Hernández Rodriguez and B. Frahm, “Digitale Seed Train Zwillinge und statistische
    Methoden,” in <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente
    Bioproduktion</i>, C. Herwig, R. Pörtner, and J. Möller, Eds. Berlin, Heidelberg:
    Springer , 2025, pp. 107–145. doi: <a href="https://doi.org/10.1007/978-3-031-75698-6_5">https://doi.org/10.1007/978-3-031-75698-6_5</a>.'
  mla: Hernández Rodriguez, Tanja, and Björn Frahm. “Digitale Seed Train Zwillinge
    und statistische Methoden.” <i>Digitale Zwillinge - Werkzeuge und Konzepte für
    intelligente Bioproduktion</i>, edited by Christoph Herwig et al., Springer ,
    2025, pp. 107–45, <a href="https://doi.org/10.1007/978-3-031-75698-6_5">https://doi.org/10.1007/978-3-031-75698-6_5</a>.
  short: 'T. Hernández Rodriguez, B. Frahm, in: C. Herwig, R. Pörtner, J. Möller (Eds.),
    Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion, Springer
    , Berlin, Heidelberg, 2025, pp. 107–145.'
  ufg: '<b>Hernández Rodriguez, Tanja/Frahm, Björn</b>: Digitale Seed Train Zwillinge
    und statistische Methoden, in: <i>Herwig, Christoph/Pörtner, Ralf/Möller, Johannes
    (Hgg.)</i>: Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion,
    Berlin, Heidelberg 2025 (Advances in Biochemical Engineering/Biotechnology), 
    S. 107–145.'
  van: 'Hernández Rodriguez T, Frahm B. Digitale Seed Train Zwillinge und statistische
    Methoden. In: Herwig C, Pörtner R, Möller J, editors. Digitale Zwillinge - Werkzeuge
    und Konzepte für intelligente Bioproduktion. Berlin, Heidelberg: Springer ; 2025.
    p. 107–45. (Advances in Biochemical Engineering/Biotechnology).'
date_created: 2026-03-27T17:45:02Z
date_updated: 2026-04-08T12:46:45Z
department:
- _id: DEP4000
doi: https://doi.org/10.1007/978-3-031-75698-6_5
editor:
- first_name: Christoph
  full_name: Herwig, Christoph
  last_name: Herwig
- first_name: Ralf
  full_name: Pörtner, Ralf
  last_name: Pörtner
- first_name: Johannes
  full_name: Möller, Johannes
  last_name: Möller
keyword:
- Bayes
- Digitaler Zwilling
- Parameterabschätzung
- Seed-Train
- Unsicherheit
language:
- iso: ger
page: 107-145
place: Berlin, Heidelberg
publication: Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion
publication_identifier:
  eisbn:
  - 978-3-031-75698-6
  isbn:
  - 978-3-031-75697-9
publication_status: published
publisher: 'Springer '
quality_controlled: '1'
series_title: Advances in Biochemical Engineering/Biotechnology
status: public
title: Digitale Seed Train Zwillinge und statistische Methoden
type: book_chapter
user_id: '83781'
year: '2025'
...
---
_id: '11377'
abstract:
- lang: eng
  text: <jats:p>consuming and often performed rather empirically. Efficient optimization
    of multiple objectives such as process time, viable cell density, number of operating
    steps &amp; cultivation scales, required medium, amount of product as well as
    product quality depicts a promising approach. This contribution presents a workflow
    which couples uncertainty-based upstream simulation and Bayes optimization using
    Gaussian processes. Its application is demonstrated in a simulation case study
    for a relevant industrial task in process development, the design of a robust
    cell culture expansion process (seed train), meaning that despite uncertainties
    and variabilities concerning cell growth, low variations of viable cell density
    during the seed train are obtained. Compared to a non-optimized reference seed
    train, the optimized process showed much lower deviation rates regarding viable
    cell densities (&lt;10% instead of 41.7%) using five or four shake flask scales
    and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall,
    it is shown that applying Bayes optimization allows for optimization of a multi-objective
    optimization function with several optimizable input variables and under a considerable
    amount of constraints with a low computational effort. This approach provides
    the potential to be used in the form of a decision tool, e.g., for the choice
    of an optimal and robust seed train design or for further optimization tasks within
    process development.
article_number: '883'
author:
- first_name: Tanja
  full_name: Hernández Rodriguez, Tanja
  id: '52466'
  last_name: Hernández Rodriguez
- first_name: Anton
  full_name: Sekulic, Anton
  last_name: Sekulic
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Björn
  full_name: Frahm, Björn
  id: '45666'
  last_name: Frahm
citation:
  ama: Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust
    Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization
    Applied to a Biopharmaceutical Seed Train Design. <i>Processes</i>. 2022;10(5).
    doi:<a href="https://doi.org/10.3390/pr10050883">10.3390/pr10050883</a>
  apa: Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., &#38; Frahm, B.
    (2022). Designing Robust Biotechnological Processes Regarding Variabilities Using
    Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.
    <i>Processes</i>, <i>10</i>(5), Article 883. <a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>
  bjps: <b>Hernández Rodriguez T <i>et al.</i></b> (2022) Designing Robust Biotechnological
    Processes Regarding Variabilities Using Multi-Objective Optimization Applied to
    a Biopharmaceutical Seed Train Design. <i>Processes</i> <b>10</b>.
  chicago: Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and
    Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities
    Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.”
    <i>Processes</i> 10, no. 5 (2022). <a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>.
  chicago-de: Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und
    Björn Frahm. 2022. Designing Robust Biotechnological Processes Regarding Variabilities
    Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.
    <i>Processes</i> 10, Nr. 5. doi:<a href="https://doi.org/10.3390/pr10050883">10.3390/pr10050883</a>,
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Hernández Rodriguez, Tanja</span>
    ; <span style="font-variant:small-caps;">Sekulic, Anton</span> ; <span style="font-variant:small-caps;">Lange-Hegermann,
    Markus</span> ; <span style="font-variant:small-caps;">Frahm, Björn</span>: Designing
    Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
    Optimization Applied to a Biopharmaceutical Seed Train Design. In: <i>Processes</i>
    Bd. 10. Basel, MDPI AG (2022), Nr. 5'
  havard: T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing
    Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
    Optimization Applied to a Biopharmaceutical Seed Train Design, Processes. 10 (2022).
  ieee: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing
    Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
    Optimization Applied to a Biopharmaceutical Seed Train Design,” <i>Processes</i>,
    vol. 10, no. 5, Art. no. 883, 2022, doi: <a href="https://doi.org/10.3390/pr10050883">10.3390/pr10050883</a>.'
  mla: Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes
    Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical
    Seed Train Design.” <i>Processes</i>, vol. 10, no. 5, 883, 2022, <a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>.
  short: T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Processes
    10 (2022).
  ufg: '<b>Hernández Rodriguez, Tanja u. a.</b>: Designing Robust Biotechnological
    Processes Regarding Variabilities Using Multi-Objective Optimization Applied to
    a Biopharmaceutical Seed Train Design, in: <i>Processes</i> 10 (2022), H. 5.'
  van: Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust
    Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization
    Applied to a Biopharmaceutical Seed Train Design. Processes. 2022;10(5).
date_created: 2024-04-25T13:35:04Z
date_updated: 2024-05-21T09:30:15Z
department:
- _id: DEP4000
doi: 10.3390/pr10050883
intvolume: '        10'
issue: '5'
keyword:
- Gaussian processes
- Bayes optimization
- Pareto optimization
- multi-objective
- cell culture
- seed train
language:
- iso: eng
place: Basel
publication: Processes
publication_identifier:
  eissn:
  - 2227-9717
publication_status: published
publisher: MDPI AG
status: public
title: Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective
  Optimization Applied to a Biopharmaceutical Seed Train Design
type: scientific_journal_article
user_id: '83781'
volume: 10
year: '2022'
...
---
_id: '10193'
abstract:
- lang: eng
  text: Development and optimization of biopharmaceutical production processes with
    cell cultures is cost- and time-consuming and often performed rather empirically.
    Efficient optimization of multiple objectives such as process time, viable cell
    density, number of operating steps & cultivation scales, required medium, amount
    of product as well as product quality depicts a promising approach. This contribution
    presents a workflow which couples uncertainty-based upstream simulation and Bayes
    optimization using Gaussian processes. Its application is demonstrated in a simulation
    case study for a relevant industrial task in process development, the design of
    a robust cell culture expansion process (seed train), meaning that despite uncertainties
    and variabilities concerning cell growth, low variations of viable cell density
    during the seed train are obtained. Compared to a non-optimized reference seed
    train, the optimized process showed much lower deviation rates regarding viable
    cell densities (<10% instead of 41.7%) using five or four shake flask scales and
    seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it
    is shown that applying Bayes optimization allows for optimization of a multi-objective
    optimization function with several optimizable input variables and under a considerable
    amount of constraints with a low computational effort. This approach provides
    the potential to be used in the form of a decision tool, e.g., for the choice
    of an optimal and robust seed train design or for further optimization tasks within
    process development.
author:
- first_name: Tanja
  full_name: Hernández Rodriguez, Tanja
  id: '52466'
  last_name: Hernández Rodriguez
- first_name: Anton
  full_name: Sekulic, Anton
  last_name: Sekulic
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Björn
  full_name: Frahm, Björn
  id: '45666'
  last_name: Frahm
citation:
  ama: 'Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing robust
    biotechnological processes regarding variabilities using multi-objective optimization
    applied to a biopharmaceutical seed train design. In: Pörtner R, Möller J, eds.
    <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>.
    Vol special issue. Processes : open access journal. MDPI; 2022:21-48. doi:<a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>'
  apa: 'Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., &#38; Frahm, B.
    (2022). Designing robust biotechnological processes regarding variabilities using
    multi-objective optimization applied to a biopharmaceutical seed train design.
    In R. Pörtner &#38; J. Möller (Eds.), <i>Bioprocess Systems Engineering Applications
    in Pharmaceutical Manufacturing: Vol. special issue</i> (pp. 21–48). MDPI. <a
    href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>'
  bjps: '<b>Hernández Rodriguez T <i>et al.</i></b> (2022) Designing Robust Biotechnological
    Processes Regarding Variabilities Using Multi-Objective Optimization Applied to
    a Biopharmaceutical Seed Train Design. In Pörtner R and Möller J (eds), <i>Bioprocess
    Systems Engineering Applications in Pharmaceutical Manufacturing</i>, vol. special
    issue. Basel: MDPI, pp. 21–48.'
  chicago: 'Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and
    Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities
    Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.”
    In <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>,
    edited by Ralf Pörtner and Johannes Möller, special issue:21–48. Processes : Open
    Access Journal. Basel: MDPI, 2022. <a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>.'
  chicago-de: 'Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und
    Björn Frahm. 2022. Designing robust biotechnological processes regarding variabilities
    using multi-objective optimization applied to a biopharmaceutical seed train design.
    In: <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>,
    hg. von Ralf Pörtner und Johannes Möller, special issue:21–48. Processes : open
    access journal. Basel: MDPI. doi:<a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Hernández Rodriguez, Tanja</span>
    ; <span style="font-variant:small-caps;">Sekulic, Anton</span> ; <span style="font-variant:small-caps;">Lange-Hegermann,
    Markus</span> ; <span style="font-variant:small-caps;">Frahm, Björn</span>: Designing
    robust biotechnological processes regarding variabilities using multi-objective
    optimization applied to a biopharmaceutical seed train design. In: <span style="font-variant:small-caps;">Pörtner,
    R.</span> ; <span style="font-variant:small-caps;">Möller, J.</span> (Hrsg.):
    <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>,
    <i>Processes : open access journal</i>. Bd. special issue. Basel : MDPI, 2022,
    S. 21–48'
  havard: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing
    robust biotechnological processes regarding variabilities using multi-objective
    optimization applied to a biopharmaceutical seed train design, in: R. Pörtner,
    J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical
    Manufacturing, MDPI, Basel, 2022: pp. 21–48.'
  ieee: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing
    robust biotechnological processes regarding variabilities using multi-objective
    optimization applied to a biopharmaceutical seed train design,” in <i>Bioprocess
    Systems Engineering Applications in Pharmaceutical Manufacturing</i>, vol. special
    issue, R. Pörtner and J. Möller, Eds. Basel: MDPI, 2022, pp. 21–48. doi: <a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>.'
  mla: Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes
    Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical
    Seed Train Design.” <i>Bioprocess Systems Engineering Applications in Pharmaceutical
    Manufacturing</i>, edited by Ralf Pörtner and Johannes Möller, vol. special issue,
    MDPI, 2022, pp. 21–48, <a href="https://doi.org/10.3390/pr10050883">https://doi.org/10.3390/pr10050883</a>.
  short: 'T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, in: R.
    Pörtner, J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical
    Manufacturing, MDPI, Basel, 2022, pp. 21–48.'
  ufg: '<b>Hernández Rodriguez, Tanja u. a.</b>: Designing robust biotechnological
    processes regarding variabilities using multi-objective optimization applied to
    a biopharmaceutical seed train design, in: <i>Pörtner, Ralf/Möller, Johannes (Hgg.)</i>:
    Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, Band
    <i>special issue</i>, Basel 2022 (Processes : open access journal),  S. 21–48.'
  van: 'Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing robust
    biotechnological processes regarding variabilities using multi-objective optimization
    applied to a biopharmaceutical seed train design. In: Pörtner R, Möller J, editors.
    Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing. Basel:
    MDPI; 2022. p. 21–48. (Processes : open access journal; vol. special issue).'
date_created: 2023-08-08T12:58:36Z
date_updated: 2023-08-16T09:24:16Z
department:
- _id: DEP4000
doi: https://doi.org/10.3390/pr10050883
editor:
- first_name: Ralf
  full_name: Pörtner, Ralf
  last_name: Pörtner
- first_name: Johannes
  full_name: Möller, Johannes
  last_name: Möller
keyword:
- Gaussian processes
- Bayes optimization
- Pareto optimization
- multi-objective
- cell culture
- seed train
language:
- iso: eng
page: 21-48
place: Basel
publication: Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing
publication_identifier:
  eisbn:
  - 978-3-0365-5209-5
  eissn:
  - 2227-9717
  isbn:
  - 978-3-0365-5210-1
publication_status: published
publisher: MDPI
quality_controlled: '1'
series_title: 'Processes : open access journal'
status: public
title: Designing robust biotechnological processes regarding variabilities using multi-objective
  optimization applied to a biopharmaceutical seed train design
type: book_chapter
user_id: '83781'
volume: special issue
year: '2022'
...
---
_id: '3349'
abstract:
- lang: eng
  text: "Model-based concepts and simulation techniques in combination with digital
    tools emerge as a key to explore the full potential of biopharmaceutical production
    processes, which contain several challenging development and process steps. One
    of these steps is the time- and cost-intensive cell proliferation process (also
    called seed train) to increase cell number from cell thawing up to production
    scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities
    in cell behavior, and influences of changes in cultivation conditions necessitate
    adequate digital solutions to provide information about the current and near future
    process state to derive correct process decisions.\r\nFor this purpose digital
    seed train twins have proved to be efficient, which digitally display the time-dependent
    behavior of important process variables based on mathematical models, strategies,
    and adaption procedures.\r\nThis chapter will outline the needs for digitalization
    of seed trains, the construction of a digital seed train twin, the role of parameter
    estimation, and different statistical methods within this context, which are applicable
    to several problems in the field of bioprocessing. The results of a case study
    are presented to illustrate a Bayesian approach for parameter estimation and prediction
    of an industrial cell culture seed train for seed train digitalization."
author:
- first_name: Tanja
  full_name: Hernández Rodriguez, Tanja
  id: '52466'
  last_name: Hernández Rodriguez
- first_name: Björn
  full_name: Frahm, Björn
  id: '45666'
  last_name: Frahm
citation:
  ama: 'Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods.
    In: Herwig C, Pörtner R, Möller J, eds. <i>Digital Twins Tools and Concepts for
    Smart Biomanufacturing</i>. Vol 176. Advances in Biochemical Engineering/Biotechnology.
    Springer; 2021:97-131. doi:<a href="https://doi.org/10.1007/10_2020_137">https://doi.org/10.1007/10_2020_137</a>'
  apa: Hernández Rodriguez, T., &#38; Frahm, B. (2021). Digital Seed Train Twins and
    Statistical Methods. In C. Herwig, R. Pörtner, &#38; J. Möller (Eds.), <i>Digital
    Twins Tools and Concepts for Smart Biomanufacturing</i> (Vol. 176, pp. 97–131).
    Springer. <a href="https://doi.org/10.1007/10_2020_137">https://doi.org/10.1007/10_2020_137</a>
  bjps: '<b>Hernández Rodriguez T and Frahm B</b> (2021) Digital Seed Train Twins
    and Statistical Methods. In Herwig C, Pörtner R and Möller J (eds), <i>Digital
    Twins Tools and Concepts for Smart Biomanufacturing</i>, vol. 176. Berlin, Heidelberg:
    Springer, pp. 97–131.'
  chicago: 'Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins
    and Statistical Methods.” In <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>,
    edited by Christoph  Herwig, Ralf  Pörtner, and Johannes  Möller, 176:97–131.
    Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer,
    2021. <a href="https://doi.org/10.1007/10_2020_137">https://doi.org/10.1007/10_2020_137</a>.'
  chicago-de: 'Hernández Rodriguez, Tanja und Björn Frahm. 2021. Digital Seed Train
    Twins and Statistical Methods. In: <i>Digital Twins Tools and Concepts for Smart
    Biomanufacturing</i>, hg. von Christoph  Herwig, Ralf  Pörtner, und Johannes  Möller,
    176:97–131. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg:
    Springer. doi:<a href="https://doi.org/10.1007/10_2020_137">https://doi.org/10.1007/10_2020_137</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Hernández Rodriguez, Tanja</span>
    ; <span style="font-variant:small-caps;">Frahm, Björn</span>: Digital Seed Train
    Twins and Statistical Methods. In: <span style="font-variant:small-caps;">Herwig,
    C.</span> ; <span style="font-variant:small-caps;">Pörtner, R.</span> ; <span
    style="font-variant:small-caps;">Möller, J.</span> (Hrsg.): <i>Digital Twins Tools
    and Concepts for Smart Biomanufacturing</i>, <i>Advances in Biochemical Engineering/Biotechnology</i>.
    Bd. 176. Berlin, Heidelberg : Springer, 2021, S. 97–131'
  havard: 'T. Hernández Rodriguez, B. Frahm, Digital Seed Train Twins and Statistical
    Methods, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digital Twins Tools and
    Concepts for Smart Biomanufacturing, Springer, Berlin, Heidelberg, 2021: pp. 97–131.'
  ieee: 'T. Hernández Rodriguez and B. Frahm, “Digital Seed Train Twins and Statistical
    Methods,” in <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>,
    vol. 176, C. Herwig, R. Pörtner, and J. Möller, Eds. Berlin, Heidelberg: Springer,
    2021, pp. 97–131. doi: <a href="https://doi.org/10.1007/10_2020_137">https://doi.org/10.1007/10_2020_137</a>.'
  mla: Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins and
    Statistical Methods.” <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>,
    edited by Christoph  Herwig et al., vol. 176, Springer, 2021, pp. 97–131, <a href="https://doi.org/10.1007/10_2020_137">https://doi.org/10.1007/10_2020_137</a>.
  short: 'T. Hernández Rodriguez, B. Frahm, in: C. Herwig, R. Pörtner, J. Möller (Eds.),
    Digital Twins Tools and Concepts for Smart Biomanufacturing, Springer, Berlin,
    Heidelberg, 2021, pp. 97–131.'
  ufg: '<b>Hernández Rodriguez, Tanja/Frahm, Björn</b>: Digital Seed Train Twins and
    Statistical Methods, in: <i>Herwig, Christoph/Pörtner, Ralf/Möller, Johannes (Hgg.)</i>:
    Digital Twins Tools and Concepts for Smart Biomanufacturing, Bd. 176, Berlin,
    Heidelberg 2021 (Advances in Biochemical Engineering/Biotechnology),  S. 97–131.'
  van: 'Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods.
    In: Herwig C, Pörtner R, Möller J, editors. Digital Twins Tools and Concepts for
    Smart Biomanufacturing. Berlin, Heidelberg: Springer; 2021. p. 97–131. (Advances
    in Biochemical Engineering/Biotechnology; vol. 176).'
date_created: 2020-08-19T07:14:11Z
date_updated: 2023-08-16T06:48:35Z
department:
- _id: DEP4021
doi: https://doi.org/10.1007/10_2020_137
editor:
- first_name: 'Christoph '
  full_name: 'Herwig, Christoph '
  last_name: Herwig
- first_name: 'Ralf '
  full_name: 'Pörtner, Ralf '
  last_name: Pörtner
- first_name: 'Johannes '
  full_name: 'Möller, Johannes '
  last_name: Möller
intvolume: '       176'
keyword:
- Bayes
- Digital twin
- Parameter estimation
- Seed train
- Uncertainty
language:
- iso: eng
page: 97–131
place: Berlin, Heidelberg
publication: Digital Twins Tools and Concepts for Smart Biomanufacturing
publication_identifier:
  eisbn:
  - 978-3-030-71660-8
  eissn:
  - 1616-8542
  isbn:
  - 978-3-030-71659-2
  issn:
  - 0724-6145
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Advances in Biochemical Engineering/Biotechnology
status: public
title: Digital Seed Train Twins and Statistical Methods
type: book_chapter
user_id: '83781'
volume: 176
year: '2021'
...
---
_id: '2394'
abstract:
- lang: eng
  text: For the production of biopharmaceuticals a seed train is required to generate
    an adequate number of cells for inoculation of the production bioreactor. This
    seed train is time- and cost-intensive but offers potential for optimization.
    A method and a protocol are described for the seed train mapping, directed modeling
    without major effort, and its optimization regarding selected optimization criteria
    such as optimal points in time for cell passaging. Furthermore, the method can
    also be applied for the set-up of a new seed train, for example for a new cell
    line. Although the chapter is directed towards suspension cell lines, the method
    is also generally applicable, e.g. for adherent cell lines.
author:
- first_name: Björn
  full_name: Frahm, Björn
  last_name: Frahm
citation:
  ama: 'Frahm B. Seed Train Optimization for Cell Culture. In: <i>Animal Cell Biotechnology</i>.
    Vol 1104. Methods in Molecular Biology . Totowa, NJ: Humana Press; 2013:355-367.
    doi:<a href="https://doi.org/10.1007/978-1-62703-733-4_22">10.1007/978-1-62703-733-4_22</a>'
  apa: 'Frahm, B. (2013). Seed Train Optimization for Cell Culture. In <i>Animal Cell
    Biotechnology</i> (Vol. 1104, pp. 355–367). Totowa, NJ: Humana Press. <a href="https://doi.org/10.1007/978-1-62703-733-4_22">https://doi.org/10.1007/978-1-62703-733-4_22</a>'
  bjps: '<b>Frahm B</b> (2013) Seed Train Optimization for Cell Culture. <i>Animal
    Cell Biotechnology</i>, vol. 1104. Totowa, NJ: Humana Press, pp. 355–367.'
  chicago: 'Frahm, Björn. “Seed Train Optimization for Cell Culture.” In <i>Animal
    Cell Biotechnology</i>, 1104:355–67. Methods in Molecular Biology . Totowa, NJ:
    Humana Press, 2013. <a href="https://doi.org/10.1007/978-1-62703-733-4_22">https://doi.org/10.1007/978-1-62703-733-4_22</a>.'
  chicago-de: 'Frahm, Björn. 2013. Seed Train Optimization for Cell Culture. In: <i>Animal
    Cell Biotechnology</i>, 1104:355–367. Methods in Molecular Biology . Totowa, NJ:
    Humana Press. doi:<a href="https://doi.org/10.1007/978-1-62703-733-4_22,">10.1007/978-1-62703-733-4_22,</a>
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Frahm, Björn</span>: Seed Train
    Optimization for Cell Culture. In: <i>Animal Cell Biotechnology</i>, <i>Methods
    in Molecular Biology </i>. Bd. 1104. Totowa, NJ : Humana Press, 2013, S. 355–367'
  havard: 'B. Frahm, Seed Train Optimization for Cell Culture, in: Animal Cell Biotechnology,
    Humana Press, Totowa, NJ, 2013: pp. 355–367.'
  ieee: 'B. Frahm, “Seed Train Optimization for Cell Culture,” in <i>Animal Cell Biotechnology</i>,
    vol. 1104, Totowa, NJ: Humana Press, 2013, pp. 355–367.'
  mla: Frahm, Björn. “Seed Train Optimization for Cell Culture.” <i>Animal Cell Biotechnology</i>,
    vol. 1104, Humana Press, 2013, pp. 355–67, doi:<a href="https://doi.org/10.1007/978-1-62703-733-4_22">10.1007/978-1-62703-733-4_22</a>.
  short: 'B. Frahm, in: Animal Cell Biotechnology, Humana Press, Totowa, NJ, 2013,
    pp. 355–367.'
  ufg: '<b>Frahm, Björn (2013)</b>: Seed Train Optimization for Cell Culture, in:
    <i>Animal Cell Biotechnology</i> (=<i>Methods in Molecular Biology  1104</i>),
    Totowa, NJ, S. 355–367.'
  van: 'Frahm B. Seed Train Optimization for Cell Culture. In: Animal Cell Biotechnology.
    Totowa, NJ: Humana Press; 2013. p. 355–67. (Methods in Molecular Biology ; vol.
    1104).'
date_created: 2020-05-19T14:45:43Z
date_updated: 2023-03-15T13:49:39Z
department:
- _id: DEP4021
doi: 10.1007/978-1-62703-733-4_22
intvolume: '      1104'
keyword:
- Seed train Optimization Modeling Prediction Space-Time-Yield (STY) Systems approach
  Bioinformatics Computational biotechnology Suspension Production
language:
- iso: eng
page: 355-367
place: Totowa, NJ
publication: Animal Cell Biotechnology
publication_identifier:
  eisbn:
  - '9781627037334'
  isbn:
  - '9781627037327'
  issn:
  - 1064-3745
  - 1940-6029
publication_status: published
publisher: Humana Press
series_title: 'Methods in Molecular Biology '
status: public
title: Seed Train Optimization for Cell Culture
type: book_chapter
user_id: '74004'
volume: 1104
year: 2013
...
---
_id: '10214'
abstract:
- lang: eng
  text: For the production of biopharmaceuticals a seed train is required to generate
    an adequate number of cells for inoculation of the production bioreactor. This
    seed train is time- and cost-intensive but offers potential for optimization.
    A method and a protocol are described for the seed train mapping, directed modeling
    without major effort, and its optimization regarding selected optimization criteria
    such as optimal points in time for cell passaging. Furthermore, the method can
    also be applied for the set-up of a new seed train, for example for a new cell
    line. Although the chapter is directed towards suspension cell lines, the method
    is also generally applicable, e.g. for adherent cell lines.
author:
- first_name: Björn
  full_name: Frahm, Björn
  id: '45666'
  last_name: Frahm
citation:
  ama: 'Frahm B. Seed Train Optimization for Cell Culture. In: Pörtner R, ed. <i>Animal
    Cell Biotechnology - Methods and Protocols</i>. Vol 1104. 3rd ed. Methods in Molecular
    Biology. Humana Press; 2013:355-367. doi:<a href="https://doi.org/10.1007/978-1-62703-733-4_22">10.1007/978-1-62703-733-4_22</a>'
  apa: Frahm, B. (2013). Seed Train Optimization for Cell Culture. In R. Pörtner (Ed.),
    <i>Animal Cell Biotechnology - Methods and Protocols</i> (3rd ed., Vol. 1104,
    pp. 355–367). Humana Press. <a href="https://doi.org/10.1007/978-1-62703-733-4_22">https://doi.org/10.1007/978-1-62703-733-4_22</a>
  bjps: '<b>Frahm B</b> (2013) Seed Train Optimization for Cell Culture. In Pörtner
    R (ed.), <i>Animal Cell Biotechnology - Methods and Protocols</i>, 3rd ed., vol.
    1104. Totowa, NJ: Humana Press, pp. 355–367.'
  chicago: 'Frahm, Björn. “Seed Train Optimization for Cell Culture.” In <i>Animal
    Cell Biotechnology - Methods and Protocols</i>, edited by Ralf Pörtner, 3rd ed.,
    1104:355–67. Methods in Molecular Biology. Totowa, NJ: Humana Press, 2013. <a
    href="https://doi.org/10.1007/978-1-62703-733-4_22">https://doi.org/10.1007/978-1-62703-733-4_22</a>.'
  chicago-de: 'Frahm, Björn. 2013. Seed Train Optimization for Cell Culture. In: <i>Animal
    Cell Biotechnology - Methods and Protocols</i>, hg. von Ralf Pörtner, 1104:355–367.
    3. Aufl. Methods in Molecular Biology. Totowa, NJ: Humana Press. doi:<a href="https://doi.org/10.1007/978-1-62703-733-4_22">10.1007/978-1-62703-733-4_22</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Frahm, Björn</span>: Seed Train
    Optimization for Cell Culture. In: <span style="font-variant:small-caps;">Pörtner,
    R.</span> (Hrsg.): <i>Animal Cell Biotechnology - Methods and Protocols</i>, <i>Methods
    in Molecular Biology</i>. Bd. 1104. 3. Aufl. Totowa, NJ : Humana Press, 2013,
    S. 355–367'
  havard: 'B. Frahm, Seed Train Optimization for Cell Culture, in: R. Pörtner (Ed.),
    Animal Cell Biotechnology - Methods and Protocols, 3rd ed., Humana Press, Totowa,
    NJ, 2013: pp. 355–367.'
  ieee: 'B. Frahm, “Seed Train Optimization for Cell Culture,” in <i>Animal Cell Biotechnology
    - Methods and Protocols</i>, 3rd ed., vol. 1104, R. Pörtner, Ed. Totowa, NJ: Humana
    Press, 2013, pp. 355–367. doi: <a href="https://doi.org/10.1007/978-1-62703-733-4_22">10.1007/978-1-62703-733-4_22</a>.'
  mla: Frahm, Björn. “Seed Train Optimization for Cell Culture.” <i>Animal Cell Biotechnology
    - Methods and Protocols</i>, edited by Ralf Pörtner, 3rd ed., vol. 1104, Humana
    Press, 2013, pp. 355–67, <a href="https://doi.org/10.1007/978-1-62703-733-4_22">https://doi.org/10.1007/978-1-62703-733-4_22</a>.
  short: 'B. Frahm, in: R. Pörtner (Ed.), Animal Cell Biotechnology - Methods and
    Protocols, 3rd ed., Humana Press, Totowa, NJ, 2013, pp. 355–367.'
  ufg: '<b>Frahm, Björn</b>: Seed Train Optimization for Cell Culture, in: <i>Pörtner,
    Ralf (Hg.)</i>: Animal Cell Biotechnology - Methods and Protocols, Bd. 1104, Totowa,
    NJ <sup>3</sup>2013 (Methods in Molecular Biology),  S. 355–367.'
  van: 'Frahm B. Seed Train Optimization for Cell Culture. In: Pörtner R, editor.
    Animal Cell Biotechnology - Methods and Protocols. 3rd ed. Totowa, NJ: Humana
    Press; 2013. p. 355–67. (Methods in Molecular Biology; vol. 1104).'
date_created: 2023-08-14T17:50:56Z
date_updated: 2023-08-16T06:41:21Z
department:
- _id: DEP4000
doi: 10.1007/978-1-62703-733-4_22
edition: '3'
editor:
- first_name: Ralf
  full_name: Pörtner, Ralf
  last_name: Pörtner
intvolume: '      1104'
keyword:
- Seed train
- Optimization
- Modeling
- Prediction
- Space-Time-Yield (STY)
- Systems approach
- Bioinformatics
- Computational biotechnology
- Suspension
- Production
language:
- iso: eng
page: 355–367
place: Totowa, NJ
publication: Animal Cell Biotechnology - Methods and Protocols
publication_identifier:
  eisbn:
  - 978-1-62703-733-4
  eissn:
  - 1940-6029
  isbn:
  - 978-1-62703-732-7
  issn:
  - 1064-3745
publication_status: published
publisher: Humana Press
series_title: Methods in Molecular Biology
status: public
title: Seed Train Optimization for Cell Culture
type: book_chapter
user_id: '83781'
volume: 1104
year: '2013'
...