Volume 9 Supplement 9

24th European Society for Animal Cell Technology (ESACT) Meeting: C2P2: Cells, Culture, Patients, Products

Open Access

Model-based strategy for cell culture seed train layout verified at lab scale

  • Simon Kern1, 2,
  • Oscar B Platas1,
  • Martin Schaletzky1,
  • Volker Sandig3,
  • Björn Frahm2 and
  • Ralf Pörtner1Email author
BMC Proceedings20159(Suppl 9):P44


Published: 14 December 2015


Production of biopharmaceuticals for diagnostic and therapeutic applications with suspension cells in bioreactors requires a seed train up to production scale [1]. For the first steps - the transitions between T-flasks, tubes, roller bottles, shake flasks, stirred bioreactors or single-use reactors - the experimental effort to lay-out these steps is high. At the same time it is known that the first cultivation steps have a significant impact on the success or failure in production scale. A software tool has been developed which provides possibilities for simulation, analysis and design of seed trains [2]. Tool structure:

  • A kinetic model. In this case a simple unstructured model where kinetic parameters can be obtained from a few experiments only.

  • A Nelder-Mead-algorithm to determine model parameters.

  • A developed MATLAB software tool able to determine optimal points in time or viable cell concentrations for transfer into the next scale.

The successful application for the cell line (AGE1.HN AAT , ProBioGen AG) has been shown previously [3]. Here the tool was tested for a suspendable CHO cell line.

Materials and methods

The cell line CHO-K1 has been grown in chemically defined TC-42 medium (TeutoCell AG, Bielefeld, Germany), 4 mmol L-1 glutamine.

Data for parameter identification for the kinetic mode were determined in shake flask cultures. The seed train steps were: 1. culture tube (0.0025 L), 2. shake flask (0.070 L), 3: Labfors 5 Cell (2 L).


For the seed train first different optimization criteria were compared in silico (Fig. 1a). Finally, the average of time at maximal space-time-yield (STY) and time at 90% of maximal growth rate (0.9·µmax) was used as optimization criterion for cell transfer. The concept was tested successfully up to a 2 L scale for 3 scale-up steps (Figure 1b).
Figure 1

(A) simulated courses of Space-Time-Yield (STY) and apparent growth rateover time exemplarily for one scale: a) point in time of minimal STY, b) average value of a) and c) as a cell passaging criterion, c) point in time of maximal STY as a cell passaging criterion, d) average of time at maximal STY and time at 0.9·µmax as a cell passaging criterion. (B) Seed train for CHO-K1 - simulated and experimental courses of viable cell density over time. Passaging of cells at the points in time calculated using average of time at maximal STY and time at 0.9·µmax (criterion d)).The seed train steps were: 1. culture tube (0.0025 L), 2. shake flask (0.070 L), 3: Labfors 5 Cell (2 L)


The concept offers a simple and inexpensive strategy for design of seed train scale-up steps. The results for the lab scale steps show that the tool was able to perform a seed train optimization only on the basis of two batches, the underlying model and its parameter identification.


The bioreactor (Labfors 5 Cell) was kindly provided by the company Infors AG, the suspendable cell line CHO-K1 by Prof. Thomas Noll, University of Bielefeld.

Authors’ Affiliations

Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology
Biotechnology & Bioprocess Engineering, Ostwestfalen-Lippe University of Applied Sciences
ProBioGen AG


  1. Eibl R, Eibl D, Pörtner R, Catapano G, Czermak P: Cell and Tissue Reaction Engineering. Springer. 2008, ISBN 978-3-540-68175-5Google Scholar
  2. Kern S, Platas-Barradas O, Pörtner R, Frahm B: Model-based strategy for cell culture seed train layout verified at lab scale. Cytotechnol. published online: 21 March 2015, DOI 10.1007/s10616-015-9858-9Google Scholar
  3. Kern S, Platas O, Schaletzky M, Sandig V, Frahm B, Pörtner R: Model-based design of the first steps of a seed train for cell culture processes. BMC Proceedings. 2013, 7 (Suppl 6): P11-(4 December 2013)PubMed CentralView ArticleGoogle Scholar


© Kern et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.