Applied Microbiology and Biotechnology

, Volume 98, Issue 16, pp 7013–7025 | Cite as

Kinetic modeling of rhamnolipid production by Pseudomonas aeruginosa PAO1 including cell density-dependent regulation

  • Marius Henkel
  • Anke Schmidberger
  • Markus Vogelbacher
  • Christian Kühnert
  • Janina Beuker
  • Thomas Bernard
  • Thomas Schwartz
  • Christoph Syldatk
  • Rudolf Hausmann
Biotechnological products and process engineering


The production of rhamnolipid biosurfactants by Pseudomonas aeruginosa is under complex control of a quorum sensing-dependent regulatory network. Due to a lack of understanding of the kinetics applicable to the process and relevant interrelations of variables, current processes for rhamnolipid production are based on heuristic approaches. To systematically establish a knowledge-based process for rhamnolipid production, a deeper understanding of the time-course and coupling of process variables is required. By combining reaction kinetics, stoichiometry, and experimental data, a process model for rhamnolipid production with P. aeruginosa PAO1 on sunflower oil was developed as a system of coupled ordinary differential equations (ODEs). In addition, cell density-based quorum sensing dynamics were included in the model. The model comprises a total of 36 parameters, 14 of which are yield coefficients and 7 of which are substrate affinity and inhibition constants. Of all 36 parameters, 30 were derived from dedicated experimental results, literature, and databases and 6 of them were used as fitting parameters. The model is able to describe data on biomass growth, substrates, and products obtained from a reference batch process and other validation scenarios. The model presented describes the time-course and interrelation of biomass, relevant substrates, and products on a process level while including a kinetic representation of cell density-dependent regulatory mechanisms.


Rhamnolipid Biosurfactant Pseudomonas aeruginosa PAO1 Process model Modeling Quorum sensing 



The authors would like to thank Dipl.-Ing. Michaela Zwick (Karlsruhe Institute of Technology, Institute of Process Engineering in Life Sciences) for excellent technical assistance. This work was financed by the Baden-Württemberg Stiftung as part of the Environmental Technology Research Program.

Supplementary material

253_2014_5750_MOESM1_ESM.pdf (153 kb)
ESM 1 (PDF 153 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Marius Henkel
    • 1
  • Anke Schmidberger
    • 2
  • Markus Vogelbacher
    • 3
  • Christian Kühnert
    • 3
  • Janina Beuker
    • 4
  • Thomas Bernard
    • 3
  • Thomas Schwartz
    • 2
  • Christoph Syldatk
    • 1
  • Rudolf Hausmann
    • 4
  1. 1.Institute of Process Engineering in Life Sciences, Section II: Technical BiologyKarlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.Institute of Functional Interfaces, Department Microbiology of Natural and Technical InterfacesKarlsruhe Institute of Technology (KIT)Eggenstein-LeopoldshafenGermany
  3. 3.Department Systems for Measurement, Control and Diagnosis (MRD)Fraunhofer Institute of Optronics, System Technologies and Image ExploitationKarlsruheGermany
  4. 4.Institute of Food Science and Biotechnology (150), Section Bioprocess Engineering (150k)University of HohenheimStuttgartGermany

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