BlueSens Report No. 1

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1 Report No. 1

2 BlueSens Report No. 1, October by BlueSens gas sensors GmbH, Herten, Germany, This report was worked out in co-operation with APZ Ruhr-Lippe, Page layout and cover design: Marcus Riepe, Krefeld, Germany, Printing press: Offset Company, Wuppertal, Germany,

3 Introduction The first BlueSens Report You may wonder why we have published a report with detailed product information and user data only recently after almost 1 successful years and several thousand sensors already sold? The answer is quite simple time. It is only recently we have increased the human resources in our PR-department and in addition we have received the support of the APZ (center for applications Biotechnik Ruhr-Lippe) which has made it possible for us to publish now. Almost every new customer wanted this type of information however until recently we referred them to our reference customers (at this point we want to thank them sincerely). Nevertheless, we knew that sooner or later we had to produce a report with case studies and information regarding our customer experience with our sensors. Consequently we contacted our customers and asked them for a case study where they describe how they use our sensors. Obviously, until recently many pharmaceutical companies were unable to cooperate due to the confidential nature of their work. However, as we do not require confidential data about the microorganisms or the specified microbial strains, we only require the official statement: Yes, we use BlueSens sensors and they operate as specified. Stunning are also statements as follows: BlueSens sensors? You don t have to explain them to me, we solely use them and no other sensor. (stated by an anonymous customer during a call). Where has this customer bought our sensors? The answer is absolutely clear of course we couldn t have achieved the global supply of our products without our sales partners or OEM-distributors, like DASGIP AG, Sartorius Stedim Biotech GmbH, Infors, Applikon, Bioengineering or many other plant developers. We also want Dr. Holger Mueller to thank them for the longtime and good cooperation. Longtime double-digit growth rates give us the motivation to continue to achieve these results in future. In order to do this, we listen to our customers and respond quickly to their needs. So our sensors are specified for each customer s application: temperature, respective gas flow or different pressure ranges we have a solution for their requirements. Thanks to the PAT-initiative of the FDA, which deals with the analysis of the process and not only with the end product, our online-sensors are wellaccepted by our customers. Our sensors can be integrated with little effort directly in the process and so are made for current requirements. We also want to take a look to the future. For aerobic fermentations, up to now, you had to connect one sensor for the measurement of CO 2 and another one for the measurement of O 2. Although that is not difficult, it would be more convenient to receive all required measurement data with one device. We have listened to our customers and have reacted to them: BlueInOne. The most compact gas analyzer on the market for the measurement of up to 4 gases with an automated pressure and humidity compensation. In this spirit I wish you to enjoy reading our report and want to thank all of our customers and sales partners for their confidence in us. BlueSens.com BlueSens Report No. 1 3

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5 Contents 6 BlueSens. Advanced information Application Reports 8 Application of a self constructed off gas analyser in the education of bioengineers Dr. Michael Maurer, FH Campus Wien 1 Continuous bio-ethanol production by means of yeast Dr.-Ing. Eva Maria del Amor Villa, Technical University Dortmund 12 Model based optimization of biogas plants Dr. F. Uhlenhut, Prof. Dr. S. Steinigeweg, Prof. Dr. A. Borchert, Prof. Dr. Reiner Lohmüller, University of Applied Sciences Emden/Leer 14 Online observation of oxygen uptake and carbon dioxide production and characterisation of oxygen transfer capacity by Prof. Dr.-Ing. Reiner Luttmann et al., Hamburg University of Applied Sciences 16 The precultivation in shake flasks for the execution of bioreactor cultivations by Prof. Dr.-Ing. Reiner Luttmann et al., Hamburg University of Applied Sciences 18 Automated Design of Experiments (DoE) in a multi-bioreactor system BIOSTAT Qplus 6 by Prof. Dr.-Ing. Reiner Luttmann et al., Hamburg University of Applied Sciences 2 Monitoring of baker s yeast fermentations PD Dr.-Ing. Lars M. Blank, Technical University Dortmund 22 Application of BlueSens Gas Analyzers in a Cell Culture Process Mathias Aehle, Martin-Luther-University Halle-Wittenberg Information 28 Connections for every application 3 BlueSens sensors overview 32 We help you understand, control and optimize your process! BCpreFerm and YieldMaster 33 The freedom of software choice FermVis and BACVis 34 Parallel systems Measuring according to PAT BlueSens.com BlueSens Report No. 1 5

6 For controlling biotechnological processes you primarily depend on continuous information. BlueSens has made it to its business to provide this information for every customer by means of gas analysis directly in the process. Reliable measurement engineering makes the results available in highest measurement density and in real time. So biotechnological processes can be analyzed better and, as a result, of course also optimized. With this report we also want to give you advanced information. Numerous examples of applications show exactly how the products of BlueSens are used under real conditions. With this booklet you can also learn concretely how our sensors are connected and readout. Furthermore, you can inform yourself about the accurate specifications of the particular sensors with the help of a clearly arranged spreadsheet. Bluesens: advanced information for your process, advanced information about the products. Nearly a decade after the foundation the dynamic company is well-known in the world of biotechnology. BlueSens stands for reasonably priced quality sensors made in Germany. The strength of the company is the personal contact to every single customer. The Managers Dr. Holger Mueller (Sales and Marketing) Dr. Udo Schmale (R&D and Production) 6

7 Every sensor a unique piece During the calibration process each sensor is tested and set up particularly. This process can take up to one week. It involves a lot of time, but it s worth it. In the detailed test procedure BlueSens solely uses certified test gases. Depending on the gas component to be measured, 1 to 18 different test gases are used. So it is assured that the sensors provide best results for each application the customer requires. Each sensor so becomes a handmade piece and is individually tested by BlueSens. BlueSens is exclusively producing the sensors in Germany. We have the highest requirements regarding the utilised components. So the company guarantees long-lasting quality and reliability of the products. Production goes hand in hand with research. By short ways the results of our Research & Development department can be integrated quickly into the production. Keeping an eye on costs BlueSens stands for sensors which are as uncomplicated as possible and therefore as competitive as possible. Based on the ever latest developments BlueSens would like to pass on its competitive edge to its customers. With the measuring systems of BlueSens the corresponding process parameters can already be determined before the actual process takes place. In the production range of active components, fermentation and also biogas generation, the productivity of the raw material can thus be optimized in preliminarily tests based on the gas measurement. In research and development BlueSens sensors mean that results are achieved faster and products can be positioned in the market quicker. The use of BlueSens sensors also means that production online can be optimized when controlling industrial processes on the spot, directly where the process takes place. This saves both personnel and production capacities and maximizes the outcome. The investment costs amortize very quickly (Return on investment). Already installed bioreactors can also be upgraded with the sensors of BlueSens with minimum effort. Therefore older installations can be modernized costeffectively. Many customers confirm ever gain: BlueSens: We cannot afford not to have it! 7

8 Application of a self constructed off gas analyser in the education of bioengineers by DI Dr. Michael Maurer, FH Campus Wien University of Applied Sciences, Bioengineering degree programme Our University of Applied Sciences, FH Campus Wien, offers a degree program in Bioengineering. In the course of this study a fermentation laboratory has to be attended. The aim of this course is the design, operation and analysis of a bioprocess experiment. The students have to use their biological, mathematical and technical skills to solve this exercise. One of the experiments involved cultivation of the methylotrophic yeast Pichia pastoris (X33); a well known host for recombinant protein expression (Cregg et al. 2), as well as for applications in white biotechnology (e.g. riboflavin (Marx et al. 28)). An overnight shake culture was used to inoculate a defined 2 l batch medium (as described in Maurer et al. 26) with 4 g glucose L-1 as sole carbon source, to a starting optical density (OD6) of 1.. The cultivation was carried out in a 5. l bioreactor (Minifors, Infors, Bottmingen-Basel, Switzerland; figure 1 B) with a tailored off gas analyser. This off gas analyser consists of a BCP-CO 2, a BCP-O 2 probe (BlueSens, Figure 1: B) bioreactor with off gas analyser Figure 1: A) self assembled off gas analyser Herten, Germany) and a mass flow controller (Vögtlin, Aesch, Switzerland) with a power supply in a separate control box (figure 1 A). The analogue signals were directly led to an I/O input of the bioreactor and measured as control parameters in the monitoring software (IRIS, Infors). The fermentation temperature was controlled at 25 C, ph was controlled at 5. with addition of 25% ammonium hydroxide and the dissolved oxygen concentration was maintained above 2% saturation by controlling the 8 BlueSens Report No. 1 BlueSens.com

9 stirrer speed between 25 and 12 rpm and the air flow between 2. and 5. l min-1. Samples were taken frequently over the whole process and analysed as described below. Three aliquots of 1 ml of culture broth were centrifuged and the supernatant saved for HPLC analysis. The pellets were washed in distilled water and recentrifuged, transferred into weighed beakers and dried at 15 C until a constant weight was attained. The biomass concentration was also monitored with an on-line probe (Fogale nanotech, Nimes, France), which had previously been calibrated with dry cell mass data (CDW). Glucose and ethanol were analysed by HPLC (Shimadzu, i DI Dr. Michael Maurer, FH Campus Wien University of Applied Sciences, Bioengineering degree programme. The University of Applied Sciences, FH Campus Wien, is an educational institution which offers a rich variety of academic studies. The bioengineering degree programme educates students for their work in the field of biotechnological industry. Figure 2: A) Trends of measured cultivation parameters glucose - (squares), ethanol (triangles) and bio mass concentration (crosses), as well as the carbon balance (circles). Figure 2: B) RQ trend read out of the P. pastoris batch cultivation. BlueSens.com BlueSens Report No. 1 9

10 Japan) using an ion exchange column Aminex HPX-87H (Bio Rad). The mobile phase was 15 mm sulphuric acid. The aim of the exercise was the calculation of typical fermentation parameters such as biomass concentration, substrate uptake rate, specific growth rate, and so on, as well as the respiratory quotient (RQ) and the over all carbon balance (OCB). Using the universal gas equation and the recorded oxygen and carbon dioxide concentration [%] and the air flow data. The students were able to calculate the oxygen uptake rate (OUR), the carbon dioxide evolution rate (CER) and hence the required RQ and OCB. Figure 2 A shows the diauxic behaviour of this yeast strain, first using up glucose as preferred substrate (specific glucose uptake rate qglucose=.44 g g-1 h-1) and forming ethanol with a rate of qp ethanol=.8 g g-1 h-1 as by product. After a first stationary phase the ethanol was utilised with a rate of qethanol =.4 g g-1 h-1. The online measurement of the oxygen and carbon dioxide concentrations enabled the simultaneous determination of the shift based on the calculated RQ, which changed from 1.2 during the aerobic glucose consumption to.5 during the ethanol utilization. The carbon utilisation was therefore balanced with a tolerance of 93-15%. These online measurements therefore serve as teaching vehicles enabling the students to grasp application and value of off-gas analysis. Literature Cregg, J., J. Cereghino, J. Shi & D. Higgins (2) Recombinant protein expression in Pichia pastoris. Mol Biotechnol, 16, Marx, H., D. Mattanovich & M. Sauer (28) Overexpression of the riboflavin biosynthetic pathway in Pichia pastoris. Microb Cell Fact, 7, 23. Maurer, M., M. Kuehleitner, B. Gasser & D. Mattanovich (26) Versatile modeling and optimization of fed batch processes for the production of secreted heterologous proteins with Pichia pastoris. MICROBIAL CELL FACTORIES, 5, -. Continuous bio-ethanol production by means of yeast by Dr.-Ing. Eva Maria del Amor Villa, Biochemical Engineering Laboratory, Biochemical and Chemical Department, Technical University Dortmund One example for applying the BlueSens technology at the Biochemical Engineering Laboratory is the gas online-monitoring for the continuous bio-ethanol production in the field of the biotechnological production of alternative fuels (so-called biofuels). Yeast is able to metabolize under anaerobic conditions several carbon sources (particularly sucrose and glucose) into carbon dioxide and ethanol, conventionally in a batch or fed batch mode. However, if the ethanol concentration exceeds the concentration threshold ca. 115 g/l, depending on the strain an inhibition of the metabolism is initiated: ethanol becomes a toxic substance and the maximum product concentration achieves a biological limit. Keeping the product content under the tolerance limit of the cells will allow increasing the bio-ethanol-yield to its maximum. i Dr.-Ing. Eva Maria del Amor Villa, Biochemical Engineering Laboratory, Biochemical and Chemical Department, Technical University Dortmund. The Biochemical Engineering Laboratory deals with research and teaching in the areas of fermentation and sterilization technology, downstream processing as well as biocatalysis (in aqueous and organic media). Pilot equipment for process scale up is available up to a fermentation capacity of 3 l for interfacing with academic and industrial partners. 1 BlueSens Report No. 1 BlueSens.com

11 Figure 1: Stirred unit reactor with connected CO 2, O 2 and ethanol sensors The continuous bio-ethanol production by means of in sodium alginate entrapped Saccharomyces cerevisiae (ATCC 7752) was successfully carried out at 4 C in a stirred bioreactor with an operating volume of 6 ml by continuous substrate feed over a period of five days. The sensors were connected gastight, allowing quantitative online records on gases (carbon dioxide, ethanol and oxygen) present in the headspace of the bioreactor (see figure 1). By using a suitable calibrated ethanol sensor a direct calculation of the ethanol content in the liquid phase could be made based on the ethanol content in the gaseous phase; those results were validated by comparative analysis using high performance liquid chromatography. The measurement of the unavoidable metabolite CO 2 in the bioreactor and the oxygen content in the flue gas stream provided the expected results (see figure 2): the CO 2 concentration increased up to 9 Vol.-% and stabilized at that value as no ambient air could enter the bioreactor. The oxygen content stagnated after reaching its minimum (approx. Vol.-%), as only CO 2 and ethanol were discharged from the system. The ethanol concentration remained almost constant after the first 6 operating hours. However, the tolerance limit for yeast with respect to ethanol was by no means reached, as it was solely intended to show that such a system could be operated over a longer period of time. The proposed measurement method offers the advantage that the analysis is not influenced by further media components and metabolites (e.g. organic acids). Strikingly, this demonstrates the potential that the arrangement used to determine online ethanol concentrations can be applied to limit the ethanol content in the medium due to an adequate adjustment. Actual works dealing with the continuous production process of bio-butanol (under anaerobic conditions) and biotensides (rhamnolipids) extent the field of application of the BlueSens technology for the gas online-monitoring in biotechnological processes. Figure 2: Gas online-monitoring of the bio-ethanol production process by continuous feed of 4 g glucose/l BlueSens.com BlueSens Report No. 1 11

12 Model based optimization of biogas plants Application Report by Dr. F. Uhlenhut, Prof. Dr. S. Steinigeweg, Prof. Dr. A. Borchert, Prof. Dr. Reiner Lohmüller, University of Applied Sciences Emden/Leer, EUTEC Institute Motivation Increasing amount of energy derived from biogas plants will only be available if a wide variety of different substrates can be used. The feed to a biogas reactor will change according to the fluctuating supply demand scenario for various substrates. The plant has to deliver maximum gas yield and hence energy yield for various substrates. This can only be achieved if the process parameters are optimized continuously. The model should be able to predict optimized process parameters as well as energy yield for a given substrate mix. Therefore the model has to take biological processes into consideration which takes place during anaerobic digestion. The aim of our research at the Emder Institut für Umwelttechnik (EUTEC) is to develop a sophisticated process model which is capable of predicting the behavior of an industrial sized biogas plant. The model should include: >>Simulation of biogas production for different substrate mixtures. >>Adaptation of appropriate modeling approaches for the simulation-based evaluation of complex substrates. >>Design of a control concept for biogas plants. Experiments Following experimental facilities have been used: Batch experiments in 1 liter flasks at 37 C for 2-3 weeks. Aim was to evaluate gas generation rate for various substrates continuous reactor in 2 liter scale. Equipped with screw pumps and BlueSens analytics system to count gas quantity and gas composition (methane and carbon dioxide) in a continuous mode. Simulation Simulation studies have been performed using ADM1 model incorporated into Matlab/Simulink. Parameters of ADM1 kinetic model have been regressed to experimental data. Results Figure 1 shows experimental results in comparison with calculated results for the continous recator in semiindustrial scale. A very good agreement between both data can be observed indicating that the model is capable of describing the complex biological processes. As input parameters only readily available data for the substrates have been used. In order to evaluate the capabilities of the model data from the biogas plant in Wittmund (Germany) have been Figure 1 Comparison of experimental (black line) and simulated data (red line) for manure (left diagram) and fat mud (right diagram). 12 BlueSens Report No. 1 BlueSens.com

13 compared to results predicted by the model (figure 2). Again just readily available parameters describing the substrate and the biogas plant have been incorporated into the process model. As can be seen a very good agreement between experimental data and data from the biogas plant have been achieved. Further research will focus on incorporating a wide variety of different substrates, to account for substrate pre-treatment and for biogas purification. i Dr. F. Uhlenhut, Prof. Dr. S. Steinigeweg, Prof. Dr. A. Borchert, Prof. Dr. Reiner Lohmüller, University of Applied Sciences Emden/Leer, EUTEC Institute. Research and development in the following areas: >> Optimization of industrial processes with respect to high level of sustainability >> Technologies to reduce pollutants in soil water and air >> Bioenergy >> Renewable resources as new raw materials Figure 2 Calculated (red line) and experimental data (black line) from industrial sized biogas plant in Wittmund (Germany). BlueSens.com BlueSens Report No. 1 13

14 Online observation of oxygen uptake and carbon dioxide production and characterisation of oxygen transfer capacity by Prof. Dr.-Ing. Reiner Luttmann et al., Research and Transfer Center of Bioprocess Engineering and Analytical Techniques Hamburg University of Applied Sciences A 5 l stainless steel bioreactor BIOSTAT ED5 was used for the production of the chemokine 1-8 del MCP-1, 1-3 del I-TAC, vmip-ii as well as for potential Malaria vaccines with the yeast Pichia pastoris in HCDC. The high instrumented reactor is equipped with BlueSens sensors for the measurement of oxygen and carbon dioxide (BCP-O 2 and BCP-CO 2 ). The sensors are placed in the offgas line of the fermenter, behind the off-gas filter. The signal for the molar fraction of oxygen xo 2 and carbon dioxide xco 2 are recorded and stored in the data acquisition system MFC- Swin. Different gas balance values are calculated with the control system and stored online. The fermentation process starts with a batch phase with unlimited growth on the substrate glycerol. In the following glycerol fed batch phase, limited cell growth is preparing the cells for the production phase on controlled methanol concentration. In figure 1 the off-gas molar fractions xo 2 and xco 2 are shown. With an air aeration the incoming molar fractions are known (xogin = xoair =.294, xcgin = xcair =.3). So the oxygen supply rate QO 2, the carbon dioxide production rate QCO 2, the respiratory quotient RQ and the oxygen transfer capacity OTC can be calculated online. The dissolved oxygen tension po 2 is controlled via po 2 / agitation control at a setpoint of 25%. The regulation starts at t = 12 h, when the po 2 drops below the setpoint. During the fed batch phase QO 2 and QCO 2 are increasing Bioreactor for recombinant protein production research exponentially proportional to the volumetric cell growth rate. The RQ converges to a stationary endpoint of.9 at batch end. With reduced cell growth both rates drop down at the beginning of the fed batch phase, but increase exponentially again afterwards. In the production phase the cell activity is reduced again. This can be observed in a decreased QO 2 and QCO 2. The oxygen transfer capacity OTC is a valuable parameter for the characterization of a bioreactor plant and a capable scale up criteria. 14 BlueSens Report No. 1 BlueSens.com

15 Figure 1: Course of off-gas measurement and gas balance values Figure 2: Course of O 2 -transfer rates during cultivation BlueSens.com BlueSens Report No. 1 15

16 In figure 2 the online estimation of the OTC and the volumetric O 2 -transfer coefficient kla are shown together with the influencing variables FnG (aeration rate) and NSt (agitation speed). Although FnG and NSt are constant in the beginning, kla and OTC are slightly decreasing. Due to the exponential cell growth during po 2 -control (since t=12 h) the oxygen uptake is increasing exponential too. Therefore the OTC has to be increased. The po 2 -controller rises FnG and later on NSt to keep the kla on track and therewith the OTC. i Prof. Dr.-Ing. Reiner Luttmann, Prof. Dr. Gesine Cornelissen, Dipl.-Ing. Ulrich Scheffler, Dipl.-Ing. Hans-Peter Bertelsen. Research and Transfer Center of Bioprocess Engineering and Analytical Techniques Hamburg University of Applied Sciences. The institute is engaged in advanced bioprocess engineering in fields such as production of potential malaria vaccines, optimization of recombinant protein production (DoE), Process Analytical Technology (PAT) and modeling and simulation of bioprocesses. The precultivation in shake flasks for the execution of bioreactor cultivations by Prof. Dr.-Ing. Reiner Luttmann et al., Research and Transfer Center of Bioprocess Engineering and Analytical Techniques Hamburg University of Applied Sciences For the execution of bioreactor cultivations the precultivation in shake flasks is from great interest. The cells should be in good condition to avoid a long adaption phase in the beginning. For assuring vital cells in the preculture no substrate and no oxygen limitation should occur during cultivation and cells should be in exponential growth. Shaking flask experiments have been carried out for the optimization of preculture conditions. Therefore a 1 l glass Erlenmeyer flask was equipped with the BluSens Sensors BCP-O 2 and BCP-CO 2 for the measurement of oxygen and carbon dioxide in the gas phase. For comparison an optical oxygen microsensor was also used. A recombinant Escherichia coli strain was cultivated. The experiments were conducted in a shaking flask cabinet at 2 rpm and 37 C. In figure 1 the course of the percentaged molar fraction of oxygen x O2 and carbon dioxide x CO2 is shown. The signal from the BlueSens O 2 -sensor (BS) is corresponding very well to the signal Figure 1: Course x O2 and x CO2 signals of shaking flask experiment of the optical sensor. The BlueSens signal however is much noiseless comparing to the other. In the beginning x O2 starts at a value around 21 % which is equal to the oxygen fraction of air (2.94 %). With increasing cell growth the oxygen demand is increasing proportional, so that the x O2 is decreasing to a value around 15.7 % at t = 6.5 h. The signal of x CO2 is contrary proportional to x O2. 16 BlueSens Report No. 1 BlueSens.com

17 Another experiment was conducted with additional measurement of the dissolved oxygen tension po 2 in the liquid phase (figure 2). This gives the opportunity for a better identification of oxygen limitation and verification of the data from the gas phase. The signals of the x O2 signals are corresponding still very well in this experiment. The po 2 is decreasing exponentially with increasing cell growth. After 4.2 hours oxygen limitation occurs. This can be seen also in the x O2 signal in a decreasing slope of the curve. At t = 6.5 h the substrate is exhausted and substrate limitation begins. The x O2 graph is at the lowest point at this time. As mentioned in the beginning, the cells should be in exponential growth and limitations should be avoided. Therefore the duration of the preculture should not exceed 3.5 hours. With an optimized preculture consistent initial conditions for bioreactor cultivations can be realized. Thus a better reproducibility and robust cultivation conditions can be achieved. Shaking flask experiments with BlueSens Sensors Figure 2: oxygen measurement in gas and liquid phase of shaking flask BlueSens.com BlueSens Report No. 1 17

18 Automated Design of Experiments (DoE) in a multi-bioreactor system BIOSTAT Qplus 6 by Prof. Dr.-Ing. Reiner Luttmann et al., Research and Transfer Center of Bioprocess Engineering and Analytical Techniques Hamburg University of Applied Sciences The bioreactor system BIO- STAT Qplus (Sartorius Stedim Biotech GmbH, Göttingen) was established in the Laboratory of Bioprocess Automation at Hamburg University of Applied Sciences. This multi-reactor system enables the execution of parallel experiments with independent measuring and control of process parameters. Therefore it is a very powerful solution for the execution of optimization experiments following DoE. The system consists of two supply towers, a digital control unit DCU 4 and six autoclavable 1 l culture vessels. Each vessel is equipped with probes for measurement of po 2, ph and foam. Two external gasmix stations with mass flow controllers are used for aeration up to 2 vvm. A pump station enables different substrate Figure 1: O 2 - and CO 2 -signals from one experiment showing all six vessels with batch phase followed by a fed batch phase limited fed-batch operations with reduced cell specific growth rates. With the BlueSens sensors BCP-O 2 for oxygen and the Multi-bioreactor system BIOSTAT Qplus 6 for the execution of DoE optimization experiments 18 BlueSens Report No. 1 BlueSens.com

19 BCP-CO 2 sensors for carbon dioxide the measurement of these gases in the off-gas of each vessel is possible. The multiplexer unit BACCom transferring the off-gas values to the process control system MFCSwin, where data are recorded and further online calculations are carried out. Experiments for the optimization of the space-time-yield of a recombinant fusion protein expressed in Escherichia coli are conducted. The process starts with a glucose batch, followed by a fed batch phase and the IPTG induced production phase. Figure 1 shows the course of the off-gas measurement of all six vessels from the multi-reactor system. The initial conditions in every single reactor are the same. Also for the batch part all parameters are identical. This can be seen in an almost identical course of the six curves in the batch phase and the very small variation of the batch end time. In the fed batch phase the cell specific growth rate µ and the liquid phase temperature JL are changed to different values (see figure 1). Also the incoming oxygen mole fraction xo 2 was increased stepwise from 2.94 % (AIR) to 45 % (AIR/O 2 ) to avoid oxygen limited cell growth. The production phase of two different DoE experiments is plotted in figure 2. For a better comparison of the two experiments the timeline of the chart is standardized onto the point of induction at the beginning of the production phase. The plot shows the observable cell specific growth rate, estimated online from the off-gas signals xo 2 and xco 2, the fluorescence signal S48/53_sol of the soluble fusion protein measured in relative fluorescence units (RFU) and the cell density cxl determined from cell dry mass. The setpoint of the cell specific growth rate µw, realized with an open loop controlled glucose fed batch, was set to.18 h-1 in experiment 1 and.21 h-1 in experiment 2. After induction the growth rate is decreasing due to the change in metabolism and a reduced liquid phase temperature in the production phase, but it is increasing afterwards and shows an almost constant course. The chosen parameters in experiment 1 yield in a much higher target protein concentration compared to experiment 2. Figure 2: -estimation with off-gas measurement and O 2 -balancing BlueSens.com BlueSens Report No. 1 19

20 Monitoring of baker s yeast fermentations by PD Dr.-Ing. Lars M. Blank, Laboratory of Chemical Biotechnology, Technical University Dortmund The open question we addressed with the new setup from BlueSens (CO 2 and ethanol sensor) i PD Dr.-Ing. Lars M. Blank, Laboratory of Chemical Biotechnology, Technical University Dortmund. The group Systems Biotechnology characterizes, designs and constructs metabolic networks. originated from our previous finding (Blank and Sauer, 24) that under aerobe and glucose excess conditions ethanol production and the rate of TCA cycle operation were dependent on the glucose uptake rate. As ethanol generally cannot be quantified in shake flasks, the finding relied only on indirect observations from 13C-tracer metabolic flux analyses. Here we aimed to directly quantify the TCA cycle flux by closing the carbon balance using the BluesSens sensors for quantification of the volatile fermentation products ethanol and CO 2. As can be seen in figure 1, the new setup delivered fermentation data of very high quality (lines represent a simultaneous fit of the experimental data using an exponential growth model). As contribution to the scientific discussion, a strong negative correlation between glucose uptake rate and the rate of TCA cycle operation could be communicated (Heyland et al., 29). The BlueSens setup was invaluable for the here presented quantitative physiology project with baker s yeast. Since then, numerous co-workers used the setup and experienced a tremendous increase in data amount and more importantly in quality. Shake flasks equipped with CO2, O2 and ethanol sensor in a waterbath shaker 2 BlueSens Report No. 1 BlueSens.com

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