Stuart M. Stocks

Modeling enzyme production with Aspergillus oryzae in pilot scale vessels with different agitation, aeration, and agitator types

The purpose of this article is to demonstrate how a model can be constructed such that the progress of a submerged fed‐batch fermentation of a filamentous fungus can be predicted with acceptable accuracy. The studied process was enzyme production with Aspergillus oryzae in 550 L pilot plant stirred tank reactors. Different conditions of agitation and aeration were employed as well as two different impeller geometries. The limiting factor for the productivity was oxygen supply to the fermentation broth, and the carbon substrate feed flow rate was controlled by the dissolved oxygen tension. In order to predict the available oxygen transfer in the system, the stoichiometry of the reaction equation including maintenance substrate consumption was first determined. Mainly based on the biomass concentration a viscosity prediction model was constructed, because rising viscosity of the fermentation broth due to hyphal growth of the fungus leads to significant lower mass transfer towards the end of the fermentation process. Each compartment of the model was shown to predict the experimental results well. The overall model can be used to predict key process parameters at varying fermentation conditions. Biotechnol. Bioeng. 2011; 108:1828–1840.

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.

Evaluation of the energy efficiency of enzyme fermentation by mechanistic modeling

Modeling biotechnological processes is key to obtaining increased productivity and efficiency. Particularly crucial to successful modeling of such systems is the coupling of the physical transport phenomena and the biological activity in one model. We have applied a model for the expression of cellulosic enzymes by the filamentous fungus Trichoderma reesei and found excellent agreement with experimental data. The most influential factor was demonstrated to be viscosity and its influence on mass transfer. Not surprisingly, the biological model is also shown to have high influence on the model prediction. At different rates of agitation and aeration as well as headspace pressure, we can predict the energy efficiency of oxygen transfer, a key process parameter for economical production of industrial enzymes. An inverse relationship between the productivity and energy efficiency of the process was found. This modeling approach can be used by manufacturers to evaluate the enzyme fermentation process for a range of different process conditions with regard to energy efficiency. Biotechnol. Bioeng. 2012; 109:950–961.

Linking hydrolysis performance to Trichoderma reesei cellulolytic enzyme profile

Trichoderma reesei expresses a large number of enzymes involved in lignocellulose hydrolysis and the mechanism of how these enzymes work together is too complex to study by traditional methods, for example, by spiking with single enzymes and monitoring hydrolysis performance. In this study, a multivariate approach, partial least squares regression, was used to see whether it could help explain the correlation between enzyme profile and hydrolysis performance. Diverse enzyme mixtures were produced by T. reesei Rut‐C30 by exploiting various fermentation conditions and used for hydrolysis of washed pretreated corn stover as a measure of enzyme performance. In addition, the enzyme mixtures were analyzed by liquid chromatography–tandem mass spectrometry to identify and quantify the different proteins. A multivariate model was applied for the prediction of enzyme performance based on the combination of different proteins present in an enzyme mixture. The multivariate model was used for identification of candidate proteins that are correlated to enzyme performance on pretreated corn stover. A very large variation in hydrolysis performance was observed and this was clearly caused by the difference in fermentation conditions. Besides β‐glucosidase, the multivariate model identified several xylanases, Cip1 and Cip2, as relevant proteins to study further. Biotechnol. Bioeng. 2016;113: 1001–1010.

Multi-parameter flow cytometry and cell sorting reveal extensive physiological heterogeneity in Bacillus cereus batch cultures.

Based on two staining protocols, DiOC6(3)/propidium iodide (PI) and RedoxSensor Green (an indicator of bacterial reductase activity)/PI, multi-parameter flow cytometry and cell sorting has identified at least four distinguishable physiological states during batch cultures of Bacillus cereus. Furthermore, dependent on the position in the growth curve, single cells gave rise to varying numbers of colonies when sorted individually onto nutrient agar plates. These growing colonies derived from a single cell had widely different lag phases, inferred from differences in colony size. This further highlights the complex population dynamics of bacterial monocultures and further demonstrates that individual bacterial cells in a culture respond in markedly dissimilar ways to the environment, resulting in a physiologically heterogenous and dynamic population.

A review of control strategies for manipulating the feed rate in fed-batch fermentation processes

A majority of industrial fermentation processes are operated in fed-batch mode. In this case, the rate of feed addition to the system is a focus for optimising the process operation, as it directly impacts metabolic activity, as well as directly affecting the volume dynamics in the system. This review covers a range of strategies which have been employed to use the feed rate as a manipulated variable in a control strategy. The feed rate is chosen as the focus for this review, as it is seen that this variable may be used towards many different objectives depending on the process of interest, the characteristics of the strain, or the product being produced, which leads to different drivers for process optimisation. This review summarises the methods, as well as focusing on the different objectives for the controllers, and the choice of measured variables involved in the strategy. The discussion includes a summary of considerations for control strategy development.

Comparison of laser diffraction and image analysis for measurement of Streptomyces coelicolor cell clumps and pellets

Morphology is important in industrial processes involving filamentous organisms because it affects the mixing and mass transfer and can be linked to productivity. Image analysis provides detailed information about the morphology but, in practice, it is often laborious including both collection of high quality images and image processing. Laser diffraction is rapid and fully automatic and provides a volume-weighted distribution of the particle sizes. However, it is based on a number of assumptions that do not always apply to samples. We have evaluated laser diffraction to measure cell clumps and pellets of Streptomyces coelicolor compare to image analysis. Samples, taken five times during fed-batch cultivation, were analyzed by image analysis and laser diffraction. The volume-weighted size distribution was calculated for each sample. Laser diffraction and image analysis yielded similar size distributions, i.e. unimodal or bimodal distributions. Both techniques produced similar estimations of the population means, whereas the estimates of the standard deviations were generally higher using laser diffraction compared to image analysis. Therefore, laser diffraction measurements are high quality and the technique may be useful when rapid measurements of filamentous cell clumps and pellets are required.

Mechanistic Fermentation Models for Process Design, Monitoring, and Control

Mechanistic models require a significant investment of time and resources, but their application to multiple stages of fermentation process development and operation can make this investment highly valuable. This Opinion article discusses how an established fermentation model may be adapted for application to different stages of fermentation process development: planning, process design, monitoring, and control. Although a longer development time is required for such modeling methods in comparison to purely data-based model techniques, the wide range of applications makes them a highly valuable tool for fermentation research and development. In addition, in a research environment, where collaboration is important, developing mechanistic models provides a platform for knowledge sharing and consolidation of existing process understanding.

Application of a Mechanistic Model as a Tool for On‐line Monitoring of Pilot Scale Filamentous Fungal Fermentation Processes‐ The Importance of Evaporation Effects

A mechanistic model‐based soft sensor is developed and validated for 550L filamentous fungus fermentations operated at Novozymes A/S. The soft sensor is comprised of a parameter estimation block based on a stoichiometric balance, coupled to a dynamic process model. The on‐line parameter estimation block models the changing rates of formation of product, biomass, and water, and the rate of consumption of feed using standard, available on‐line measurements. This parameter estimation block, is coupled to a mechanistic process model, which solves the current states of biomass, product, substrate, dissolved oxygen and mass, as well as other process parameters including kLa, viscosity and partial pressure of CO2. State estimation at this scale requires a robust mass model including evaporation, which is a factor not often considered at smaller scales of operation. The model is developed using a historical data set of 11 batches from the fermentation pilot plant (550L) at Novozymes A/S. The model is then implemented on‐line in 550L fermentation processes operated at Novozymes A/S in order to validate the state estimator model on 14 new batches utilizing a new strain. The product concentration in the validation batches was predicted with an average root mean sum of squared error (RMSSE) of 16.6%. In addition, calculation of the Janus coefficient for the validation batches shows a suitably calibrated model. The robustness of the model prediction is assessed with respect to the accuracy of the input data. Parameter estimation uncertainty is also carried out. The application of this on‐line state estimator allows for on‐line monitoring of pilot scale batches, including real‐time estimates of multiple parameters which are not able to be monitored on‐line. With successful application of a soft sensor at this scale, this allows for improved process monitoring, as well as opening up further possibilities for on‐line control algorithms, utilizing these on‐line model outputs. Biotechnol. Bioeng. 2017;114: 589–599.

A novel model-based control strategy for aerobic filamentous fungal fed-batch fermentation processes

A novel model‐based control strategy has been developed for filamentous fungal fed‐batch fermentation processes. The system of interest is a pilot scale (550 L) filamentous fungus process operating at Novozymes A/S. In such processes, it is desirable to maximize the total product achieved in a batch in a defined process time. In order to achieve this goal, it is important to maximize both the product concentration, and also the total final mass in the fed‐batch system. To this end, we describe the development of a control strategy which aims to achieve maximum tank fill, while avoiding oxygen limited conditions. This requires a two stage approach: (i) calculation of the tank start fill; and (ii) on‐line control in order to maximize fill subject to oxygen transfer limitations. First, a mechanistic model was applied off‐line in order to determine the appropriate start fill for processes with four different sets of process operating conditions for the stirrer speed, headspace pressure, and aeration rate. The start fills were tested with eight pilot scale experiments using a reference process operation. An on‐line control strategy was then developed, utilizing the mechanistic model which is recursively updated using on‐line measurements. The model was applied in order to predict the current system states, including the biomass concentration, and to simulate the expected future trajectory of the system until a specified end time. In this way, the desired feed rate is updated along the progress of the batch taking into account the oxygen mass transfer conditions and the expected future trajectory of the mass. The final results show that the target fill was achieved to within 5% under the maximum fill when tested using eight pilot scale batches, and over filling was avoided. The results were reproducible, unlike the reference experiments which show over 10% variation in the final tank fill, and this also includes over filling. The variance of the final tank fill is reduced by over 74%, meaning that it is possible to target the final maximum fill reproducibly. The product concentration achieved at a given set of process conditions was unaffected by the control strategy. Biotechnol. Bioeng. 2017;114: 1459–1468.