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

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

Henkel, M. / Schmidberger, A. / Vogelbacher, M. / Kühnert, C. / Beuker, J. / Bernard, T. / Schwartz, T. / Syldatk, C. / Hausmann, R. (2014)

place:

Applied Microbiology and Biotechnology 98 (2014), 16, 7013-7025

Date: 2014

Henkel, M. / Schmidberger, A. / Vogelbacher, M. / Kühnert, C. / Beuker, J. / Bernard, T. / Schwartz, T. / Syldatk, C. / Hausmann, R. (2014): „Kinetic modeling of rhamnolipid production by Pseudomonas aeruginosa PAO1 including cell density-dependent regulation“. In: Applied Microbiology and Biotechnology 98 (2014), 16, 7013-7025

Abstract

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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.