Evaluation of productive biofilms for continuous lactic acid production.

In white biotechnology research, the putative superiority of productive biofilms to conventional biotransformation processes based on planktonic cultures has been increasingly discussed in recent years. In the present study, we chose lactic acid production as a model application to evaluate biofilm potential. A pure culture of Lactobacillus bacteria was grown in a tubular biofilm reactor. The biofilm system was cultivated monoseptically in a continuous mode for more than 3 weeks. The higher cell densities that could be obtained in the continuous biofilm system compared with the planktonic culture led to a significantly increased space-time yield. The productivity reached 80% of the maximum value 10 days after start-up and no subsequent decline was observed, confirming the suitability of the system for long-term fermentation. The analysis of biofilm performance revealed that productivity increases with the flow velocity. This is explained by the reduced retention time of the liquid phase in the reactor, and, thus, a minor pH drop caused by the released lactic acid. At low flow velocities, the pH drops to a value where growth and production are significantly inhibited. The biofilm was visualized by magnetic resonance imaging to analyze biofilm thickness. To deepen the understanding of the biofilm system, we used a simple model for cell growth and lactic acid production.

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations.

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l(-1) biomass and 0.8 g l(-1) total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.

In situ near infrared spectroscopy for analyte-specific monitoring of glucose and ammonium in streptomyces coelicolor fermentations.

There are many challenges associated with in situ collection of near infrared (NIR) spectra in a fermentation broth, particularly for highly aerated and agitated fermentations with filamentous organisms. In this study, antibiotic fermentation by the filamentous bacterium Streptomyces coelicolor was used as a model process. Partial least squares (PLS) regression models were calibrated for glucose and ammonium based on NIR spectra collected in situ. To ensure that the models were calibrated based on analyte-specific information, semisynthetic samples were used for model calibration in addition to data from standard batches. Thereby, part of the inherent correlation between the analytes could be eliminated. The set of semisynthetic samples were generated from fermentation broth from five separate fermentations to which different amounts of glucose, ammonium, and biomass were added. This method has previously been used off line but never before in situ. The use of semisynthetic samples along with validation on an independent batch provided a critical and realistic evaluation of analyte-specific models based on in situ NIR spectroscopy. The prediction of glucose was highly satisfactory resulting in a RMSEP of 1.1 g/L. The prediction of ammonium based on NIR spectra collected in situ was not satisfactory. A comparison with models calibrated based on NIR spectra collected off line suggested that this is caused by signal attenuation in the optical fibers in the region above 2,000 nm; a region which contains important absorption bands for ammonium. For improved predictions of ammonium in situ, it is suggested to focus efforts on enhancing the signal in that particular region.

On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors.

Batch bioreactor cultivations using Saccharomyces cerevisiae at high (190-305 gl(-1) glucose) or low (21-25 gl(-1) glucose) gravity conditions were monitored on-line using multi-wavelength fluorescence (MWF) and standard monitoring sensors. Partial least squares models were calibrated for the prediction of cell dry weight (CDW), ethanol and consumed glucose, using the two data types separately. The low gravity cultivations (LGCs) consisted of two phases (glucose consumption with concomitant ethanol production followed by ethanol consumption after glucose depletion), which proved difficult to model using one and the same model for both phases. Segmented modelling, using different models for the two phases, improved the predictions significantly. The prediction models calibrated on standard on-line process data displayed similar or lower root mean square error of prediction (RMSEP) compared to the fluorescence models. The best prediction models for high gravity cultivations (HGCs) had RMSEPs of 1.0 gl(-1) CDW, 1.8 gl(-1) ethanol and 5.0 gl(-1) consumed glucose, corresponding to 4%, 2% and 2% of the respective concentration intervals. Corresponding numbers in low gravity models were 0.3 gl(-1) CDW, 0.7 gl(-1) ethanol and 1.0 gl(-1) consumed glucose, corresponding to 4%, 8% and 4% of the respective concentration intervals.

Matrix notation for efficient development of first-principles models within PAT applications: integrated modeling of antibiotic production with Streptomyces coelicolor.

A matrix notation coupled to macroscopic principles is introduced as a means to develop first- principles models in an efficient and structured way within PAT applications. The notation was evaluated for developing an integrated biological, chemical (pH modeling) and physical (gas-liquid exchange) model for describing antibiotic production with Streptomyces coelicolor in batch fermentations. The model provided statistically adequate fits to all the monitored macroscopic biological, chemical and physical data of the process, except the phosphate uptake dynamics. This phosphate discrepancy is hypothesized to result from the internal storage of phosphate as polyphosphate prior to the exponential growth phase. The antibiotic production was associated with the stationary phase and its kinetics was adequately described using a modified Luedeking-Piret equation. Further, the maintenance was best described by employing a combination of Pirt and Herbert models, a result that was supported by a model-based hypothesis testing. Overall the process knowledge currently incorporated in the model is believed to be useful both for process optimization purposes and for further testing of hypotheses aiming at improving the mechanistic understanding of antibiotic production with S. coelicolor. Last but not least, the matrix notation is believed to be a promising supporting tool for efficient development and communication of complex dynamic models within a PAT framework.

Chemoenzymatic dynamic kinetic resolution of acyloins.

[Reaction: see text]. Acyloins (alpha-hydroxy ketones) are important building blocks in organic synthesis, e.g., for the total synthesis of epothilones. Optically pure acyloins can be obtained by lipase-catalyzed kinetic resolution (KR) of the racemate with, for example, Burkholderia cepacia lipase, but this process suffers from a yield limitation of 50%. To devise a dynamic kinetic resolution (DKR), we studied the racemization of two different acyloins and corresponding esters with various amine bases and ion exchangers. No combination of base and solvent was found that could selectively racemize the acyloin or corresponding ester under the conditions needed for a DKR. In contrast to bases, acidic resins (ARs) were found to racemize the acyloins selectively in n-hexane and in water. Unfortunately, the AR deactivated the lipase, preventing a one-pot DKR. Minor side reactions involving the AR, the substrate acyloin, and the vinyl ester acyl donor were also observed. However, an efficient DKR was made possible by the spatial separation of lipase and ion exchanger, with enzymatic transesterification and AR-catalyzed racemization taking place simultaneously in two compartments connected by a pump loop. The conversion of substrate alcohol was 91%, the selectivity toward the product butyrate ester 90%, and the enantiomeric excess of the (S)-product 93% ee.