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

Abstract

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.

High‐Performance Production of Biosurfactant Rhamnolipid with Nitrogen Feeding

“…This great rhamnolipid productivity was improved by about 50% when compared with that obtained in a previous study using the same strain (He et al, ), and even higher than the rates obtained by several studies performed in bioreactors (Henkel et al, ; Müller et al, , ). It should also be mentioned that the rhamnolipid productivity in the present sequential fed‐batch fermentation without extra NaNO 3 addition was higher than that obtained by a previous study due to the higher carbon source used (4.5% versus 3.0%).…”

Microbial production of rhamnolipids: opportunities, challenges and strategies

“…Several studies had focused on exploring various fermentation strategies for enhancing rhamnolipid yield by optimization of the fermentation parameters. Currently, many of the proposed methods target on optimization of the components in the growth medium using optimization techniques such as response surface methodology [ 64 ]. However, fermentation parameters including the type and feeding profile of substrates, pH, temperature, aeration rate, dissolved oxygen, cell density and availability of in situ product removal, are essential prerequisites for setting up efficient fermentation strategy as these parameters can significantly impact on the rhamnolipid yield [ 29 , 65 ].…”

Characterization of Biosurfactant Produced by a Novel Strain of Pseudomonas aeruginosa, Isolate ADMT1

“…The C 10 chain in the fatty acid component is predominant in the case of P. aeruginosa (Pornsunthorntawee et al, ). The production of RL by strains of Pseudomonas depends on the various physicochemical factors such as pH (Khaje Bafghi & Fazaelipoor, ; Kosaric, ), temperature (Wei, Chou, & Chang, ), carbon source (Desai & Banat, ), cell density (Henkel et al, ), and others. Therefore, the optimization of RL production is necessary.…”