---
_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: '12812'
abstract:
- lang: eng
  text: Discerning unexpected from expected data patterns is the key challenge of
    anomaly detection. Although a multitude of solutions has been applied to this
    modern Industry 4.0 problem, it remains an open research issue to identify the
    key characteristics subjacent to an anomaly, sc. generate hypothesis as to why
    they appear. In recent years, machine learning models have been regarded as universal
    solution for a wide range of problems. While most of them suffer from non-self-explanatory
    representations, Gaussian Processes (GPs) deliver interpretable and robust statistical
    data models, which are able to cope with unreliable, noisy, or partially missing
    data. Thus, we regard them as a suitable solution for detecting and appropriately
    representing anomalies and their respective characteristics. In this position
    paper, we discuss the problem of automatic and interpretable anomaly detection
    by means of GPs. That is, we elaborate on why GPs are well suited for anomaly
    detection and what the current challenges are when applying these probabilistic
    models to large-scale production data.
author:
- first_name: Fabian
  full_name: Berns, Fabian
  last_name: Berns
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Christian
  full_name: Beecks, Christian
  last_name: Beecks
citation:
  ama: Berns F, Lange-Hegermann M, Beecks C. <i>Towards Gaussian Processes for Automatic
    and Interpretable Anomaly Detection in Industry 4.0</i>. (Panetto H, Madani K,
    Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92.
    doi:<a href="https://doi.org/10.5220/0010130300870092">10.5220/0010130300870092</a>
  apa: Berns, F., Lange-Hegermann, M., &#38; Beecks, C. (2020). Towards Gaussian Processes
    for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto,
    K. Madani, &#38; A. Smirnov (Eds.), <i> Proceedings of the International Conference
    on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>
    (pp. 87–92). SCITEPRESS - Science and Technology Publications. <a href="https://doi.org/10.5220/0010130300870092">https://doi.org/10.5220/0010130300870092</a>
  bjps: <b>Berns F, Lange-Hegermann M and Beecks C</b> (2020) <i>Towards Gaussian
    Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>,
    Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.
  chicago: Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. <i>Towards
    Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry
    4.0</i>. Edited by H. Panetto, K. Madani, and A. Smirnov. <i> Proceedings of the
    International Conference on Innovative Intelligent Industrial Production and Logistics
    IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications, 2020.
    <a href="https://doi.org/10.5220/0010130300870092">https://doi.org/10.5220/0010130300870092</a>.
  chicago-de: Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. <i>Towards
    Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry
    4.0</i>. Hg. von H. Panetto, K. Madani, und A. Smirnov. <i> Proceedings of the
    International Conference on Innovative Intelligent Industrial Production and Logistics
    IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications. doi:<a
    href="https://doi.org/10.5220/0010130300870092">10.5220/0010130300870092</a>,
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Berns, Fabian</span> ; <span
    style="font-variant:small-caps;">Lange-Hegermann, Markus</span> ; <span style="font-variant:small-caps;">Beecks,
    Christian</span> ; <span style="font-variant:small-caps;">Panetto, H.</span> ;
    <span style="font-variant:small-caps;">Madani, K.</span> ; <span style="font-variant:small-caps;">Smirnov,
    A.</span> (Hrsg.): <i>Towards Gaussian Processes for Automatic and Interpretable
    Anomaly Detection in Industry 4.0</i> : SCITEPRESS - Science and Technology Publications,
    2020'
  havard: F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for
    Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science
    and Technology Publications, 2020.
  ieee: 'F. Berns, M. Lange-Hegermann, and C. Beecks, <i>Towards Gaussian Processes
    for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. SCITEPRESS
    - Science and Technology Publications, 2020, pp. 87–92. doi: <a href="https://doi.org/10.5220/0010130300870092">10.5220/0010130300870092</a>.'
  mla: Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable
    Anomaly Detection in Industry 4.0.” <i> Proceedings of the International Conference
    on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>,
    edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications,
    2020, pp. 87–92, <a href="https://doi.org/10.5220/0010130300870092">https://doi.org/10.5220/0010130300870092</a>.
  short: F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic
    and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and
    Technology Publications, 2020.
  ufg: '<b>Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian</b>: Towards Gaussian
    Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg.
    von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.'
  van: Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic
    and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov
    A, editors.  Proceedings of the International Conference on Innovative Intelligent
    Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and
    Technology Publications; 2020.
conference:
  end_date: 2020-11-04
  location: Budapest, HUNGARY
  name: International Conference on Innovative Intelligent Industrial Production and
    Logistics (IN4PL)
  start_date: 2020-11-02
date_created: 2025-04-17T06:20:07Z
date_updated: 2025-06-26T13:31:38Z
department:
- _id: DEP5000
doi: 10.5220/0010130300870092
editor:
- first_name: H.
  full_name: Panetto, H.
  last_name: Panetto
- first_name: K.
  full_name: Madani, K.
  last_name: Madani
- first_name: A.
  full_name: Smirnov, A.
  last_name: Smirnov
keyword:
- Anomaly Detection
- Gaussian Processes
- Explainable Machine Learning
- Industry 4.0
language:
- iso: eng
page: 87-92
publication: ' Proceedings of the International Conference on Innovative Intelligent
  Industrial Production and Logistics IN4PL - Volume 1'
publication_identifier:
  isbn:
  - 978-989-758-476-3
publication_status: published
publisher: SCITEPRESS - Science and Technology Publications
status: public
title: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection
  in Industry 4.0
type: conference_editor_article
user_id: '83781'
year: '2020'
...