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Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae
Journal of Biotechnology 160 (2012) 195–201
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Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec
Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae Richard Biener ∗ , Anne Steinkämper, Thomas Horn University of Applied Sciences Esslingen, Department of Natural Sciences, Kanalstraße 33, 73728 Esslingen, Germany
a r t i c l e
i n f o
Article history: Received 15 November 2011 Received in revised form 22 February 2012 Accepted 9 March 2012 Available online 17 March 2012 Keywords: Calorimetry Heat flow Saccharomyces cerevisiae Specific growth rate Fed-batch Control
a b s t r a c t The specific growth rate of a Saccharomyces cerevisiae strain with glucose as limiting C-source was estimated from the measured heat flow produced by the cells. For the cultivation a standard 30 l laboratory bioreactor was used, which was extended in such a way that heat balancing is possible. The feed rate was adjusted by a feedforward/feedback controller such that the specific growth rate was kept on the desired set-point value. On the basis of experimental investigations it was demonstrated that the specific growth rate can be controlled at a given set point value below the critical value to prevent the production of growth-inhibitory ethanol due to the Crabtree effect. With this control strategy high biomass concentrations of more than 110 g l−1 can be obtained. © 2012 Elsevier B.V. All rights reserved.
1. Introduction During the batch cultivation of Baker’s yeast Saccharomyces cerevisiae when glucose or other sugars are present in concentrations above a critical value, ethanol is produced even under fully aerobic process conditions. This phenomenon is known as the Crabtree effect and is caused by a limited respiratory capacity (Fiechter et al., 1981; Sonnleitner and Käppeli, 1986). The formation of ethanol is disadvantageous for industrial processes because ethanol is growth inhibiting and contains a large part of the energy content of the sugars used. Yeast cells are frequently used for the production of recombinant proteins. In contrast to Escherichia coli cells yeast cells allow folding and glycosylation of recombinant proteins (Demain and Vaishnav, 2009; Mendoza-Vega et al., 1994). In order to achieve high productivities of recombinant proteins high-cell-density fedbatch cultivation processes (HCDC) are used (Shang et al., 2008; van Hoek et al., 2000). To be able to reach high cell densities the formation of growth inhibitory by-products such as ethanol has to be minimized. This can be achieved by keeping the sugar concentration under a critical value during the fed-batch process. This critical glucose concentration was reported to be between 4 and 150 mg l−1 (Woehrer and Roehr, 1981). This value corresponds to a critical specific growth rate between 0.18 and 0.3 h−1 (Woehrer and Roehr, 1981). In actual practice, measurement and control of such low
∗ Corresponding author. Fax: +49 711 397 3533. E-mail address: [email protected] (R. Biener). 0168-1656/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jbiotec.2012.03.006
glucose concentrations are difficult and seldom performed (Landgrebe et al., 2010; Mazarevica et al., 2004; O’Connor et al., 1992). Therefore, control variables which are more easily measured and still represent cultivation performance, are of interest. Among others the following control variables are used in practice: respiratory quotient (RQ), ethanol production rate, ethanol concentration, growth rate, or oxygen uptake rate (O’Connor et al., 1992). A very prominent control strategy is the control of the respiratory quotient (RQ) (Aiba et al., 1976). The RQ can be determined online by an off gas analysis system by dividing the carbon dioxide evolution rate (CER) by the oxygen uptake rate (OUR). When the RQ is well above 1 ethanol is produced. If the RQ is controlled at values between 1 and 1.2 no ethanol is formed (Fiechter et al., 1981; Woehrer and Roehr, 1981). Besides control variables derived from an off gas analysis system control variables resulting from the online measured heat produced by the cells present an alternative. Heat released by microorganisms during cultivation in a bioreactor provides information about the stoichiometry and kinetics of growth and product formation (Marison and von Stockar, 1986, 1988; Maskow et al., 2011; MeierSchneiders et al., 1995; von Stockar et al., 2006; van Kleeff et al., 1993). Since it immediately indicates changes in cell metabolism it can be considered as an effective control variable (Schubert et al., 2007). Calorimetric control of fed-batch processes with S. cerevisiae was described by Larsson et al. (1991) and Randolph et al. (1990). Randolph et al. (1990) combined cellular heat flow with CER measurements from an offgas analysis device to estimate the
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respiratory quotient (RQ) which was subsequently controlled. In Larsson et al. (1991) a probing control strategy was used. When glucose was exhausted, the heat production rate dropped. At this point a pulse of glucose was added. Recently, the calorimetric control of the specific growth rate for Crabtree negative yeast cells was described by Schuler et al. (2012). This work describes the application of a calorimetric method to directly control the specific growth rate during the fed-batch cultivation of a Crabtree positive strain of S. cerevisiae. This control strategy was successfully developed in a previous work for a HCDC of a recombinant E. coli strain producing green fluorescent protein (GFP) (Biener et al., 2010). The main objective of the present contribution is to demonstrate the feasibility of applying this calorimetric control strategy to yeast cultivation. For the cultivation a standard 30 l laboratory bioreactor was used, which was extended in such a way that heat balancing was possible. The heat flow generated by the microorganisms during the cultivation can be determined on-line reliably with this calorimetric setup. A state observer was used to estimate the specific growth rate of the microorganisms in real-time. The feed rate was adjusted by a controller such that the specific growth rate is kept on the desired set point value (Biener et al., 2010). The same control scheme was used in this work for the S. cerevisiae cultivation. Only the controller parameters had to be adapted. 2. Materials and methods 2.1. Strains S. cerevisiae type strain (DSM 70449, DSMZ, Braunschweig, Germany) was used for all cultivations. The strain was preserved as 1.8 ml aliquots in a 20 g l−1 glycerol solution at −40 ◦ C. These frozen stock cultures were used for inoculum preparation. 2.2. Growth media and culture conditions The cells were precultured in 3000-ml shaking flasks with four baffles containing 500 ml chemically defined medium. The flasks were incubated at 30 ◦ C on a shaker for 24 h. The preculture and production medium had the following composition (in g l−1 ): glucose (24); (NH4 )2 SO4 (15); KH2 PO4 (8); MgSO4 ·7H2 O (3); CaCl2 ·2H2 O (29 × 10−3 ); ZnSO4 ·7H2 O (57 × 10−3 ); MnCl2 ·2H2 O (3.6 × 10−3 ); Na2 -EDTA (38 × 10−3 ); FeSO4 ·7H2 O (28 × 10−3 ); CuSO4 ·5H2 O (5 × 10−3 ); CoCl2 ·6H2 O (4.7 × 10−3 ); MoO3 (4.8 × 10−3 ); H3 BO4 (3 × 10−3 ); KI (0.3 × 10−3 ); biotin (0.6 × 10−3 ); Ca-pantothenate (12 × 10−3 ); nicotinamide (12 × 10−3 ); inositol (300 × 10−3 ); thiamine-HCl (12 × 10−3 ); pyridoxine-HCl (12 × 10−3 ); p-aminobenzoic acid (2.4 × 10−3 ). The preculture was used to inoculate the bioreactor. The initial volume was 7 l. After glucose or ethanol depletion, a concentrated feed solution was fed using the feeding strategy described below. The feed solution had the following composition (in g l−1 ): glucose (500); (NH4 )2 SO4 (9); MgSO4 ·7H2 O (2.5); K2 SO4 (3.5); Na2 SO4 (0.28); CaCl2 ·2H2 O (29 × 10−3 ); ZnSO4 ·7H2 O (57 × 10−3 ); MnCl2 ·2H2 O (3.6 × 10−3 ); Na2 -EDTA (38 × 10−3 ); FeSO4 ·7H2 O (28 × 10−3 ); CuSO4 ·5H2 O (5 × 10−3 ); CoCl2 ·6H2 O (4.7 × 10−3 ); MoO3 (4.8 × 10−3 ); H3 BO4 (3 × 10−3 ); KI (0.3 × 10−3 ); biotin (0.6 × 10−3 ); Ca-pantothenate (12 × 10−3 ); nicotinamide (12 × 10−3 ); inositol (300 × 10−3 ); thiamine-HCl (12 × 10−3 ); pyridoxine-HCl (12 × 10−3 ); p-aminobenzoic acid (2.4 × 10−3 ). 2.3. Analyses Samples were taken every 30–60 min manually or using an auto sampler (HiTec Zang GmbH, Herzogenrath) connected to a microfiltration probe (TRACE Analytics GmbH, Braunschweig). Optical
density was measured by an Eppendorf BioPhotometer at 600 nm after proper dilution. Cell dry weights were determined by filling balanced Eppendorf-tubes with 10-ml samples. The cell pellets were washed in saline solution (0.9% NaCl solution) and dried until constancy of weight was achieved. For analyses of supernatant component samples were inactivated at 80 ◦ C for 10 min and centrifuged at 16,000 × g for 15 min. Enzyme test kits from r-Biopharm, Darmstadt were used for the determination of ethanol in supernatants from culture samples. Glucose concentration was determined enzymatically using BIOSEN C line (EKF-diagnostic GmbH, Barleben). The concentrations of oxygen and carbon dioxide in the offgas were measured by electrochemical and infrared gas analysis systems, respectively (Tandem, Magellan Instruments Ltd). These values were used to calculate the oxygen uptake rate (OUR), carbon dioxide evolution arte (CER), and the respiratory quotient (RQ).
2.4. Bioreactor and calorimetric setup The configuration of the experimental setup is shown in Fig. 1. The bioreactor used was a standard stirred tank lab bioreactor (Model NLF 22, 30 l, Bioengineering AG, Wald, Switzerland). The maximum working volume is 20 l. The jacketed stainless-steel vessel was thermally insulated with mineral wool. Agitation was achieved through two 6-blade Rushton turbine impellers driven by a DC motor controlled by a speed controller (SIC). The liquid level in the reactor was determined by measuring the pressure difference between the bottom and the top of the vessel. The reactor temperature Tr and the jacket inlet and outlet temperatures Tji and Tjo were measured using PT100 probes Class A (IEC 751 guideline). Reactor temperature was kept at 30 ◦ C by cascade control using the jacket inlet temperature as the manipulated variable as described in Biener et al. (2010). Jacket inlet temperature is controlled either by heating with an electric heater or by cooling via a heat exchanger which is supplied with tap water. This lead to a very precise temperature control (Tr < 10 mK) which is crucial for the heat flow calculations (Biener et al., 2010; Voisard et al., 2002). The ambient temperature Te of the reactor was measured offline during the experiments and ranged from 19.5 to 21.5 ◦ C. The mass flow of the jacket water circuit was measured using a Coriolis flow meter (Endress und Hauser, Weil am Rhein, Germany). To improve the accuracy of the heat flow calculation, the mass flow was maintained at a constant value of 800 kg h−1 (FIC). The pH was controlled at 5.0 using ammonia water and phosphoric acid. Antifoam (silicone oil) was fed when required. The DO was controlled by a multivariate controller such that the DO value was always above 25% air saturation as described in Biener et al. (2010). The manipulated variables were stirrer speed, air flow rate, O2 -flow rate and pressure, respectively. The gas flow rates were controlled by thermal mass flow controllers (Red-y, Vögtlin, Aesch, Switzerland). Turbidity was measured on-line using a NIR absorption probe (optek-Danulat GmbH, Essen). The feed rate was controlled gravimetrically (WIC). The stirring power was measured using a Wattmeter (DSL – electronic GmbH, Viersen) between the stirrer motor and the power supply. The calibration procedure was described in Biener et al. (2010). The process control software BioSCADALab from Bioengineering AG was used. BioSCADALab communicates with the IFM control units via Profibus technology. The nonlinear adaptive controller for specific growth rate control, the heat balance model and the state observer for estimation of specific growth rate were implemented in Matlab. The communication between Matlab and the process control software was realized via a SQL database.
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Fig. 1. Configuration of the bioreactor used for the calorimetric experiments. Variables are transferred to Matlab where the heat balance model and the controller are ˙ j , jacket water mass flow; Pmot , stirrer motor power; app , implemented. Symbols: Tr , reactor temperature; Tji , jacket inlet temperature; Tjo , jacket outlet temperature, m estimated specific growth rate; TIC (temperature control); WIC (gravimetric control of the feed rate); FIC (mass flow control); SIC (stirrer speed control).
2.5. Heat balance model and control of the specific growth rate
water when leaving the reactor. These effects were summarized for air by the equation
The heat balance model described in detail in our previous work (Biener et al., 2010) was used to determine the heat flow produced by the cells. The standard bioreactor was used to integrate the calorimetric measurement principles and to perform the experiments. This way no extra microcalorimeters or reaction calorimeters have to be used. The heat flow produced by the cells, qp can be calculated from qp = qj − qs + qg + qe + qf .
(1)
where qj is the heat flow to the reactor jacket, qs the heat generated by the stirrer, qg the heat flow induced by gassing, qe the heat loss to the environment, and qf the heat flow caused by feeding a substrate solution. The volume specific heat of reaction is defined by qp Qp = . V
(2)
The heat flow from the reactor interior to the reactor jacket qj was calculated by ˙ j · cp,j · (Tji − Tjo ) qj = m
(3)
where Tji is the jacket water inlet temperature, Tjo the jacket water ˙ j the mass flow of the jacket water circuit and outlet temperature, m cp,j its specific heat capacity. The effect of gassing on the heat balance was also included. Air or oxygen entering the bioreactor are heated up and carry evaporated
qg = Fair · air · cp,air · (Tair,o − Tair,i ) + Fair · air · (go · hv,o − gi · hv,i ) (4) with the aeration rate Fair , the density air , the specific heat capacity cp,air , the inlet and outlet temperatures Tair,i and Tair,o , the moisture content g and the enthalpy of vaporization hv . A corresponding equation was also used for oxygen. The heat loss to the environment was calculated assuming a constant heat transfer coefficient Ue qe = Ue · Ae · (Tr − Te )
(5)
where Te is the bioreactor ambient temperature. The heat flow by feeding solution was determined by ˙ f · cp,f · (Tr − Tf ) qf = m
(6)
˙ f. with the feed temperature Tf and the feed rate m The heat flow by acid addition qac and CO2 stripping qCO2 were shown to play a minor role (Voisard et al., 2002) and were not considered here. The maximum estimated errors of the heat flows with this setup are within the range of 10–20 W (Biener et al., 2010) which is low compared to the heat evolution rate generated by the yeast cells. This can be more than 1200 W.
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Fig. 2. Time profiles of process parameters during a fed-batch cultivation of S. cerevisiae. The apparent specific growth rate was controlled calorimetrically at a set point value of 0.2 h−1 after glucose and ethanol were depleted A: heat production rate, feed rate and apparent specific growth rate; B: dry cell weight, glucose and ethanol; C: heat production rate and RQ; and D: major heat flows.
The specific growth rate was estimated from the heat flow generated by the cells using a state observer as described in Biener et al. (2010) based on the equation dqp = app · qp dt
(7)
Here the variable app is denoted as apparent specific growth rate since the real specific growth rate may differ from this value. For the control of the specific growth rate a feedforward/feedback controller developed in Biener et al. (2010) was used. The feed rate is calculated by F = F0 + Fb
F0 =
sp + mS YX/S
·
V0 · cX,0 sp ·(t−t ) 0 ·e cS,f
3.1. Application of the calorimetric controller to cultivations of S. cerevisiae: feeding start after ethanol depletion
(9)
In Fig. 2 the time profiles of cultivation parameters for a calorimetrically controlled fed-batch culture of S. cerevisiae with a constant set-point for the specific growth rate (denoted as run A) are shown. The first part of the batch phase lasts 10 h until glucose was depleted (Fig. 2B). Biomass concentration increased exponentially while glucose concentration decreased during this phase. Due to the Crabtree effect ethanol was produced and its concentration reached 10 g l−1 at the time when glucose was exhausted. The heat production rate during this phase in Fig. 2A was always lower than 5 W l−1 and drops to a value below 1 W l−1 after glucose was depleted. The apparent specific growth rate consequently becomes negative. During the second part of the batch phase the produced ethanol and presumably other non-measured by-products such as organic acids were subsequently consumed. The ethanol concentration is almost constant between 10 h and 15 h. Nevertheless, biomass concentration increased exponentially but at a lower specific growth rate compared to growth on glucose. This means that another by-product was consumed during this phase.
with the set point value for the specific growth rate sp , the glucose feed concentration cS,f , and the value for the biomass concentration at feeding start cX,0 . The estimated values for the yield coefficient of glucose YX/S was 0.5 g g−1 and for the cellular maintenance mS was 0.1 g g−1 h−1 . The feedback part of the controller Fb is a standard PI algorithm:
Fb = KR ·
e+
1 · TN
e · d
(10)
with the control deviation e e = sp − app
3. Results
(8)
where F0 is the feed forward part (Riesenberg, 1991)
the integral time 30 min was used. The apparent specific growth rate used here was estimated from the heat flow measurements as described above. The controller gain was adapted automatically with increasing biomass concentration. The controller is started automatically after glucose limitation where the depletion of glucose is detected automatically by a sharp increase in the DO signal. To make sure that this increase is not only momentarily, the controller start is delayed by 30 min.
(11)
For the application to S. cerevisiae cultivations only the controller parameters (controller gain and integral time) had to be adjusted. The starting value for the proportional gain KR was 0.05 and for
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Fig. 3 compares the apparent and real specific growth rates during run A after feeding start. The real specific growth rate was determined from the dry cell weight data. The mean apparent specific growth rate app was 0.2 ± 0.03 h−1 . It is in good agreement with the mean value for the real specific growth rate which was 0.19 ± 0.03 h−1 .
3.2. Application of the calorimetric controller to cultivations of S. cerevisiae: feeding start after glucose depletion
Fig. 3. Comparison of apparent and real specific growth rate during a fed-batch cultivation of S. cerevisiae (run A). The apparent specific growth rate was controlled calorimetrically at a set point value of 0.2 h−1 after glucose and ethanol were depleted. The time profile of the measured biomass concentration is also shown.
Similar to this observation, biomass is still growing after ethanol is limited which supports the assumption that other by-products besides ethanol were produced during the batch phase. Possible by-products produced by S. cerevisiae are among others pyruvate, acetate and glycerol (Dijken et al., 1993; Postma et al., 1989). Heat production rate also increased exponentially to reach a value of 10 W l−1 . The estimated apparent specific growth rate fluctuates during this phase suggesting the successive consumption of different by-products produced from glucose during the preceding batch phase. This phase is finished after 22 h. The end is indicated by a sharp drop of the heat production rate to <1 W l−1 . The feeding was started automatically 30 min after this drop. At feeding start ethanol was depleted. During the first hour of the fed-batch phase only the feed-forward part of the controller was active to allow the system to settle. Then the feed-back part was switched on automatically. The set point for the specific growth rate was set to 0.2 h−1 which is less than the critical value of crit of approximately 0.3 h−1 (Woehrer and Roehr, 1981) to prevent the formation of by-products. Another reason for keeping the set point for the specific growth rate at moderate values is to prevent a limitation in the oxygen supply. To avoid oxygen limitation the reactor was operated temporarily at the maximum agitation rate of 1450 rpm, and the aeration gas was enriched with pure oxygen via mass flow controllers. At the beginning of the feeding the specific growth rate reached the set point value. The heat flow rate per unit volume reached a maximum value of 65 W l−1 at the end of the cultivation at 32 h and the dry cell weight concentration reached a final value of 55 g l−1 . During the fed-batch phase the glucose concentration was always lower than 0.5 g l−1 which is in the range of the critical value considering the measurement accuracy. No ethanol production was observed. The controller action is reflected in the time profile of the feed rate in Fig. 2A. The comparison of the RQ value with the cellular heat flow rate in Fig. 2C shows that after glucose limitation and before feeding start the RQ value is well below 1, indicating the consumption of ethanol. After feeding start when the specific growth rate is controlled below the critical value and no ethanol was produced, the RQ remains between 1 and 1.2, as expected. The major heat sinks and sources are shown in Fig. 2D. As seen from the data the most important heat flow is the heat flow to the reactor jacket followed by the heat generated by the stirrer. The heat flows due to gassing and the loss to the environment are of minor importance. The same is valid also for the heat flow by feeding solution which is therefore not included in the diagram.
During the second run B (Fig. 4) a different feeding strategy was used. While in run A the feeding was started after ethanol and presumably other by-products were exhausted, the feeding was started in run B shortly after the cells were glucose-limited after 8 h (Fig. 4A and B). The intention of this experiment was to show that the specific growth rate can be calorimetrically controlled with glucose feed even when ethanol is present. Initially, the set point for the specific growth rate was set to 0.25 h−1 . During the succeeding 8 h no ethanol was produced due to the limited supply of glucose. Moreover, ethanol and glucose were metabolized simultaneously by the cells and after 17 h ethanol was exhausted. The ethanol depletion is followed by a sharp decrease in the heat production rate and the apparent specific growth rate. The controller consequently increases the feed rate to compensate ethanol by glucose and to reach again the set-point value of the specific growth rate. The set-point was subsequently decreased to 0.18 h−1 to avoid a limitation in the oxygen supply. The controller was able to control the apparent specific growth rate to the set point values with a mean control deviation of approximately 0.05 h−1 (Fig. 4A). A final dry cell weight concentration of 110 g l−1 and a maximum heat flow rate of 85 W l−1 were achieved. Compared to run A the space-time yield was almost 3 times higher and reached a value of 4.5 g/(l h). The RQ value in Fig. 4C decreased gradually from 1.05 to 0.6 after feeding start until ethanol was exhausted. During the fed-batch phase the RQ was constant at a value of around 1.0. As in run A the heat flow to the reactor jacket is dominating the heat balance followed by the heat generated by the stirrer (Fig. 4D).
4. Discussion and conclusion The calorimetric controller which was developed for a recombinant E. coli strain in our previous work was successfully applied for HCDC of a S. cerevisiae strain in a standard bioreactor. The same calorimetric setup was used. Only the controller parameters had to be adjusted. During an HCDC the heat flow generated by the cells is high due to high cell densities. This makes the application of calorimetric methods advantageous. The sensitivity of calorimetric measurements increases with increasing biomass. At the beginning of the fermentation runs during the batch phase the cellular heat production rate is near the detection limit. However, during the fed-batch phase when the heat balance model is used to control the specific growth rate, the resolution of the heat production rate signal is sufficient for the control strategy. The apparent specific growth rate app was controlled at values smaller than the critical value crit , in order to avoid the Crabtree effect and therefore an unwanted production of growth-inhibitory by-products such as ethanol. Considering the measurement accuracy, the mean value of the apparent specific growth rate was in good agreement with the mean value of the real specific growth rate. This means that the specific growth rate could be estimated from the heat flow generated by the microorganisms. Since the cells remain in a constant metabolic state, no further measurements were necessary as input to the control scheme. However, attention has to be paid to the control of DO so that always an aerobic regime is guaranteed.
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Fig. 4. Time profiles of process parameters during a fed-batch cultivation of S. cerevisiae. The apparent specific growth rate was controlled calorimetrically after glucose was depleted after 8 h. The set point value for was 0.25 h−1 between 8 and 22 h and then changed to 0.18 h−1 A: heat production rate, feed rate and apparent specific growth rate; B: dry cell weight, glucose and ethanol; C: heat production rate and RQ; and D: major heat flows.
The apparent specific growth rate could be estimated using the cellular heat flow qp and the controller was able to maintain app reliably at the set point value. During process perturbations the apparent specific growth rate changed rapidly due to a sudden increase or decrease in the heat flow rate. A resulting negative value of the apparent specific growth rate is not necessarily an indicator of dying cells. In fact, rapid changes in heat flow and apparent specific growth rate reflect rapid changes in metabolism due to a change in the environmental conditions which may be the result of process perturbations. As a consequence of this fast response the controller can quickly adjust the feed rate such that the set point is reached again. When glucose is the only C-source in the medium during the fed-batch phase and no ethanol is produced, the RQ obtained from off-gas analysis is in the range of 1.1–1.2. The calorimetric control of the specific growth rate is in this case equivalent to the control of RQ. However, when ethanol and glucose are metabolized simultaneously, the RQ value drops continuously from 1.0 to 0.6 (Woehrer and Roehr, 1981). In this situation the calorimetric controller may have advantages over the RQ controller because the set-point of the specific growth rate is still constant while the RQ value is changing. If the RQ is controlled at a value between 1 and 1.2 ethanol is not formed but also not consumed (Woehrer and Roehr, 1981). Hence, the growth yield of calorimetric control is in this case higher since all the C-sources were used. Furthermore, the application of RQ data as sensitive control variable for S. cerevisiae fermentation requires a careful design (Shang et al., 2006). Another advantage of direct control of the specific growth rate is that the specification of the set-point may also consider the boundary conditions of a limited oxygen and heat transfer. The performance of the calorimetric control system is similar to traditional control strategies with respect to biomass concentrations achieved. van Hoek et al. (2000) attained 130 g l−1 after
48 h using an open loop control with an exponential feeding profile. Shang et al. (2006) used a RQ control strategy and gained 120 g l−1 after 65 h. Wang et al. (2010) applied a feedback strategy using an ethanol sensor and the DO value and reached 102 g l−1 after 38 h. With the calorimetric controller we achieved 110 g l−1 after 24 h. Certainly, further research needs to be carried out, especially in terms of improving the sensitivity of the calorimetric system in the standard bioreactor. Possible approaches include the improvement of temperature control, reduction of the jacket water circuit mass flow, optimization of controller tuning and enhancement of reactor insulation. A higher sensitivity of the calorimetric model would allow reducing the initial glucose concentration and therefore improve the calorimetric control performance. The calorimetric control strategy uses only easy measurable process parameters such as temperatures and flow rates. No complex and sensitive on-line analysis technologies are necessary. Therefore, the calorimetric method presents an effective alternative to the RQ control strategy from OUR or CER measurements via gas analysis. Hence, there is great potential for its application in industrial bioprocesses with standard bioreactors. Nomenclature
A CER ci cp DO e F g hv
heat exchanging area (m2 ) CO2 evolution rate (mmol l−1 h−1 ) concentration of component i (g l−1 ) specific heat capacity (J g−1 K−1 ) dissolved oxygen concentration (% air saturation) control deviation volumetric flow (l s−1 ) moisture content in gas (g g−1 ) enthalpy of vaporization of water (J kg−1 )
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m ˙ m ms n OUR q P Q RQ T U V Y
mass (g) mass flow (g s−1 ) substrate requirement for maintenance (g g−1 h−1 ) stirrer speed (rpm) oxygen uptake rate (mmol l−1 h−1 ) heat flow (W) electrical power (W) volumetric heat flow (W l−1 ) Respiratory quotient temperature (K) heat transfer coefficient (W m−2 K−1 ) volume (l) yield coefficient
Greek symbols density (kg m−3 ) specific growth rate (h−1 ) Subscripts 0 feed forward ac acid air air app apparent b feed back CO2 CO2 e environment feeding f g gassing i inlet jacket j mot stirrer motor O2 oxygen o outlet process p r reactor stirrer s sp set point biomass X Acknowledgement The authors would like to thank Carolin Lauxen and Christian Krauß for excellent assistance. References Aiba, S., Nagai, S., Nishizawa, Y., 1976. Fed batch culture of Saccharomyces cerevisiae: a perspective of computer control to enhance the productivity in Baker’s yeast cultivation. Biotechnol. Bioeng. 18, 1001–1016. Biener, R., Steinkämper, A., Hofmann, J., 2010. Calorimetric control for high cell density cultivation of a recombinant Escherichia coli strain. J. Biotechnol. 146, http://www.sciencedirect.com/science/article/B6T3C-4Y65S7S-2/2/ 45–53, 4ca63f0d7f9fc41b5a618bd62428e461. Demain, A.L., Vaishnav, P., 2009. Production of recombinant proteins by microbes and higher organisms. Biotechnol. Adv. 27, 297–306. Dijken, J.P., Weusthuis, R.A., Pronk, J.T., 1993. Kinetics of growth and sugar consumption in yeasts. Antonie Van Leeuwenhoek 63, 343–352. Fiechter, A., Fuhrmann, G.F., Kappeli, O., 1981. Regulation of glucose metabolism in growing yeast cells. Adv. Microb. Physiol. 22, 123–183. Landgrebe, D., Haake, C., Höpfner, T., Beutel, S., Hitzmann, B., Scheper, T., Rhiel, M., Reardon, K.F., 2010. On-line infrared spectroscopy for bioprocess monitoring. Appl. Microbiol. Biotechnol. 88, 11–22.
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Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec
Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae Richard Biener ∗ , Anne Steinkämper, Thomas Horn University of Applied Sciences Esslingen, Department of Natural Sciences, Kanalstraße 33, 73728 Esslingen, Germany
a r t i c l e
i n f o
Article history: Received 15 November 2011 Received in revised form 22 February 2012 Accepted 9 March 2012 Available online 17 March 2012 Keywords: Calorimetry Heat flow Saccharomyces cerevisiae Specific growth rate Fed-batch Control
a b s t r a c t The specific growth rate of a Saccharomyces cerevisiae strain with glucose as limiting C-source was estimated from the measured heat flow produced by the cells. For the cultivation a standard 30 l laboratory bioreactor was used, which was extended in such a way that heat balancing is possible. The feed rate was adjusted by a feedforward/feedback controller such that the specific growth rate was kept on the desired set-point value. On the basis of experimental investigations it was demonstrated that the specific growth rate can be controlled at a given set point value below the critical value to prevent the production of growth-inhibitory ethanol due to the Crabtree effect. With this control strategy high biomass concentrations of more than 110 g l−1 can be obtained. © 2012 Elsevier B.V. All rights reserved.
1. Introduction During the batch cultivation of Baker’s yeast Saccharomyces cerevisiae when glucose or other sugars are present in concentrations above a critical value, ethanol is produced even under fully aerobic process conditions. This phenomenon is known as the Crabtree effect and is caused by a limited respiratory capacity (Fiechter et al., 1981; Sonnleitner and Käppeli, 1986). The formation of ethanol is disadvantageous for industrial processes because ethanol is growth inhibiting and contains a large part of the energy content of the sugars used. Yeast cells are frequently used for the production of recombinant proteins. In contrast to Escherichia coli cells yeast cells allow folding and glycosylation of recombinant proteins (Demain and Vaishnav, 2009; Mendoza-Vega et al., 1994). In order to achieve high productivities of recombinant proteins high-cell-density fedbatch cultivation processes (HCDC) are used (Shang et al., 2008; van Hoek et al., 2000). To be able to reach high cell densities the formation of growth inhibitory by-products such as ethanol has to be minimized. This can be achieved by keeping the sugar concentration under a critical value during the fed-batch process. This critical glucose concentration was reported to be between 4 and 150 mg l−1 (Woehrer and Roehr, 1981). This value corresponds to a critical specific growth rate between 0.18 and 0.3 h−1 (Woehrer and Roehr, 1981). In actual practice, measurement and control of such low
∗ Corresponding author. Fax: +49 711 397 3533. E-mail address: [email protected] (R. Biener). 0168-1656/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jbiotec.2012.03.006
glucose concentrations are difficult and seldom performed (Landgrebe et al., 2010; Mazarevica et al., 2004; O’Connor et al., 1992). Therefore, control variables which are more easily measured and still represent cultivation performance, are of interest. Among others the following control variables are used in practice: respiratory quotient (RQ), ethanol production rate, ethanol concentration, growth rate, or oxygen uptake rate (O’Connor et al., 1992). A very prominent control strategy is the control of the respiratory quotient (RQ) (Aiba et al., 1976). The RQ can be determined online by an off gas analysis system by dividing the carbon dioxide evolution rate (CER) by the oxygen uptake rate (OUR). When the RQ is well above 1 ethanol is produced. If the RQ is controlled at values between 1 and 1.2 no ethanol is formed (Fiechter et al., 1981; Woehrer and Roehr, 1981). Besides control variables derived from an off gas analysis system control variables resulting from the online measured heat produced by the cells present an alternative. Heat released by microorganisms during cultivation in a bioreactor provides information about the stoichiometry and kinetics of growth and product formation (Marison and von Stockar, 1986, 1988; Maskow et al., 2011; MeierSchneiders et al., 1995; von Stockar et al., 2006; van Kleeff et al., 1993). Since it immediately indicates changes in cell metabolism it can be considered as an effective control variable (Schubert et al., 2007). Calorimetric control of fed-batch processes with S. cerevisiae was described by Larsson et al. (1991) and Randolph et al. (1990). Randolph et al. (1990) combined cellular heat flow with CER measurements from an offgas analysis device to estimate the
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respiratory quotient (RQ) which was subsequently controlled. In Larsson et al. (1991) a probing control strategy was used. When glucose was exhausted, the heat production rate dropped. At this point a pulse of glucose was added. Recently, the calorimetric control of the specific growth rate for Crabtree negative yeast cells was described by Schuler et al. (2012). This work describes the application of a calorimetric method to directly control the specific growth rate during the fed-batch cultivation of a Crabtree positive strain of S. cerevisiae. This control strategy was successfully developed in a previous work for a HCDC of a recombinant E. coli strain producing green fluorescent protein (GFP) (Biener et al., 2010). The main objective of the present contribution is to demonstrate the feasibility of applying this calorimetric control strategy to yeast cultivation. For the cultivation a standard 30 l laboratory bioreactor was used, which was extended in such a way that heat balancing was possible. The heat flow generated by the microorganisms during the cultivation can be determined on-line reliably with this calorimetric setup. A state observer was used to estimate the specific growth rate of the microorganisms in real-time. The feed rate was adjusted by a controller such that the specific growth rate is kept on the desired set point value (Biener et al., 2010). The same control scheme was used in this work for the S. cerevisiae cultivation. Only the controller parameters had to be adapted. 2. Materials and methods 2.1. Strains S. cerevisiae type strain (DSM 70449, DSMZ, Braunschweig, Germany) was used for all cultivations. The strain was preserved as 1.8 ml aliquots in a 20 g l−1 glycerol solution at −40 ◦ C. These frozen stock cultures were used for inoculum preparation. 2.2. Growth media and culture conditions The cells were precultured in 3000-ml shaking flasks with four baffles containing 500 ml chemically defined medium. The flasks were incubated at 30 ◦ C on a shaker for 24 h. The preculture and production medium had the following composition (in g l−1 ): glucose (24); (NH4 )2 SO4 (15); KH2 PO4 (8); MgSO4 ·7H2 O (3); CaCl2 ·2H2 O (29 × 10−3 ); ZnSO4 ·7H2 O (57 × 10−3 ); MnCl2 ·2H2 O (3.6 × 10−3 ); Na2 -EDTA (38 × 10−3 ); FeSO4 ·7H2 O (28 × 10−3 ); CuSO4 ·5H2 O (5 × 10−3 ); CoCl2 ·6H2 O (4.7 × 10−3 ); MoO3 (4.8 × 10−3 ); H3 BO4 (3 × 10−3 ); KI (0.3 × 10−3 ); biotin (0.6 × 10−3 ); Ca-pantothenate (12 × 10−3 ); nicotinamide (12 × 10−3 ); inositol (300 × 10−3 ); thiamine-HCl (12 × 10−3 ); pyridoxine-HCl (12 × 10−3 ); p-aminobenzoic acid (2.4 × 10−3 ). The preculture was used to inoculate the bioreactor. The initial volume was 7 l. After glucose or ethanol depletion, a concentrated feed solution was fed using the feeding strategy described below. The feed solution had the following composition (in g l−1 ): glucose (500); (NH4 )2 SO4 (9); MgSO4 ·7H2 O (2.5); K2 SO4 (3.5); Na2 SO4 (0.28); CaCl2 ·2H2 O (29 × 10−3 ); ZnSO4 ·7H2 O (57 × 10−3 ); MnCl2 ·2H2 O (3.6 × 10−3 ); Na2 -EDTA (38 × 10−3 ); FeSO4 ·7H2 O (28 × 10−3 ); CuSO4 ·5H2 O (5 × 10−3 ); CoCl2 ·6H2 O (4.7 × 10−3 ); MoO3 (4.8 × 10−3 ); H3 BO4 (3 × 10−3 ); KI (0.3 × 10−3 ); biotin (0.6 × 10−3 ); Ca-pantothenate (12 × 10−3 ); nicotinamide (12 × 10−3 ); inositol (300 × 10−3 ); thiamine-HCl (12 × 10−3 ); pyridoxine-HCl (12 × 10−3 ); p-aminobenzoic acid (2.4 × 10−3 ). 2.3. Analyses Samples were taken every 30–60 min manually or using an auto sampler (HiTec Zang GmbH, Herzogenrath) connected to a microfiltration probe (TRACE Analytics GmbH, Braunschweig). Optical
density was measured by an Eppendorf BioPhotometer at 600 nm after proper dilution. Cell dry weights were determined by filling balanced Eppendorf-tubes with 10-ml samples. The cell pellets were washed in saline solution (0.9% NaCl solution) and dried until constancy of weight was achieved. For analyses of supernatant component samples were inactivated at 80 ◦ C for 10 min and centrifuged at 16,000 × g for 15 min. Enzyme test kits from r-Biopharm, Darmstadt were used for the determination of ethanol in supernatants from culture samples. Glucose concentration was determined enzymatically using BIOSEN C line (EKF-diagnostic GmbH, Barleben). The concentrations of oxygen and carbon dioxide in the offgas were measured by electrochemical and infrared gas analysis systems, respectively (Tandem, Magellan Instruments Ltd). These values were used to calculate the oxygen uptake rate (OUR), carbon dioxide evolution arte (CER), and the respiratory quotient (RQ).
2.4. Bioreactor and calorimetric setup The configuration of the experimental setup is shown in Fig. 1. The bioreactor used was a standard stirred tank lab bioreactor (Model NLF 22, 30 l, Bioengineering AG, Wald, Switzerland). The maximum working volume is 20 l. The jacketed stainless-steel vessel was thermally insulated with mineral wool. Agitation was achieved through two 6-blade Rushton turbine impellers driven by a DC motor controlled by a speed controller (SIC). The liquid level in the reactor was determined by measuring the pressure difference between the bottom and the top of the vessel. The reactor temperature Tr and the jacket inlet and outlet temperatures Tji and Tjo were measured using PT100 probes Class A (IEC 751 guideline). Reactor temperature was kept at 30 ◦ C by cascade control using the jacket inlet temperature as the manipulated variable as described in Biener et al. (2010). Jacket inlet temperature is controlled either by heating with an electric heater or by cooling via a heat exchanger which is supplied with tap water. This lead to a very precise temperature control (Tr < 10 mK) which is crucial for the heat flow calculations (Biener et al., 2010; Voisard et al., 2002). The ambient temperature Te of the reactor was measured offline during the experiments and ranged from 19.5 to 21.5 ◦ C. The mass flow of the jacket water circuit was measured using a Coriolis flow meter (Endress und Hauser, Weil am Rhein, Germany). To improve the accuracy of the heat flow calculation, the mass flow was maintained at a constant value of 800 kg h−1 (FIC). The pH was controlled at 5.0 using ammonia water and phosphoric acid. Antifoam (silicone oil) was fed when required. The DO was controlled by a multivariate controller such that the DO value was always above 25% air saturation as described in Biener et al. (2010). The manipulated variables were stirrer speed, air flow rate, O2 -flow rate and pressure, respectively. The gas flow rates were controlled by thermal mass flow controllers (Red-y, Vögtlin, Aesch, Switzerland). Turbidity was measured on-line using a NIR absorption probe (optek-Danulat GmbH, Essen). The feed rate was controlled gravimetrically (WIC). The stirring power was measured using a Wattmeter (DSL – electronic GmbH, Viersen) between the stirrer motor and the power supply. The calibration procedure was described in Biener et al. (2010). The process control software BioSCADALab from Bioengineering AG was used. BioSCADALab communicates with the IFM control units via Profibus technology. The nonlinear adaptive controller for specific growth rate control, the heat balance model and the state observer for estimation of specific growth rate were implemented in Matlab. The communication between Matlab and the process control software was realized via a SQL database.
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Fig. 1. Configuration of the bioreactor used for the calorimetric experiments. Variables are transferred to Matlab where the heat balance model and the controller are ˙ j , jacket water mass flow; Pmot , stirrer motor power; app , implemented. Symbols: Tr , reactor temperature; Tji , jacket inlet temperature; Tjo , jacket outlet temperature, m estimated specific growth rate; TIC (temperature control); WIC (gravimetric control of the feed rate); FIC (mass flow control); SIC (stirrer speed control).
2.5. Heat balance model and control of the specific growth rate
water when leaving the reactor. These effects were summarized for air by the equation
The heat balance model described in detail in our previous work (Biener et al., 2010) was used to determine the heat flow produced by the cells. The standard bioreactor was used to integrate the calorimetric measurement principles and to perform the experiments. This way no extra microcalorimeters or reaction calorimeters have to be used. The heat flow produced by the cells, qp can be calculated from qp = qj − qs + qg + qe + qf .
(1)
where qj is the heat flow to the reactor jacket, qs the heat generated by the stirrer, qg the heat flow induced by gassing, qe the heat loss to the environment, and qf the heat flow caused by feeding a substrate solution. The volume specific heat of reaction is defined by qp Qp = . V
(2)
The heat flow from the reactor interior to the reactor jacket qj was calculated by ˙ j · cp,j · (Tji − Tjo ) qj = m
(3)
where Tji is the jacket water inlet temperature, Tjo the jacket water ˙ j the mass flow of the jacket water circuit and outlet temperature, m cp,j its specific heat capacity. The effect of gassing on the heat balance was also included. Air or oxygen entering the bioreactor are heated up and carry evaporated
qg = Fair · air · cp,air · (Tair,o − Tair,i ) + Fair · air · (go · hv,o − gi · hv,i ) (4) with the aeration rate Fair , the density air , the specific heat capacity cp,air , the inlet and outlet temperatures Tair,i and Tair,o , the moisture content g and the enthalpy of vaporization hv . A corresponding equation was also used for oxygen. The heat loss to the environment was calculated assuming a constant heat transfer coefficient Ue qe = Ue · Ae · (Tr − Te )
(5)
where Te is the bioreactor ambient temperature. The heat flow by feeding solution was determined by ˙ f · cp,f · (Tr − Tf ) qf = m
(6)
˙ f. with the feed temperature Tf and the feed rate m The heat flow by acid addition qac and CO2 stripping qCO2 were shown to play a minor role (Voisard et al., 2002) and were not considered here. The maximum estimated errors of the heat flows with this setup are within the range of 10–20 W (Biener et al., 2010) which is low compared to the heat evolution rate generated by the yeast cells. This can be more than 1200 W.
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Fig. 2. Time profiles of process parameters during a fed-batch cultivation of S. cerevisiae. The apparent specific growth rate was controlled calorimetrically at a set point value of 0.2 h−1 after glucose and ethanol were depleted A: heat production rate, feed rate and apparent specific growth rate; B: dry cell weight, glucose and ethanol; C: heat production rate and RQ; and D: major heat flows.
The specific growth rate was estimated from the heat flow generated by the cells using a state observer as described in Biener et al. (2010) based on the equation dqp = app · qp dt
(7)
Here the variable app is denoted as apparent specific growth rate since the real specific growth rate may differ from this value. For the control of the specific growth rate a feedforward/feedback controller developed in Biener et al. (2010) was used. The feed rate is calculated by F = F0 + Fb
F0 =
sp + mS YX/S
·
V0 · cX,0 sp ·(t−t ) 0 ·e cS,f
3.1. Application of the calorimetric controller to cultivations of S. cerevisiae: feeding start after ethanol depletion
(9)
In Fig. 2 the time profiles of cultivation parameters for a calorimetrically controlled fed-batch culture of S. cerevisiae with a constant set-point for the specific growth rate (denoted as run A) are shown. The first part of the batch phase lasts 10 h until glucose was depleted (Fig. 2B). Biomass concentration increased exponentially while glucose concentration decreased during this phase. Due to the Crabtree effect ethanol was produced and its concentration reached 10 g l−1 at the time when glucose was exhausted. The heat production rate during this phase in Fig. 2A was always lower than 5 W l−1 and drops to a value below 1 W l−1 after glucose was depleted. The apparent specific growth rate consequently becomes negative. During the second part of the batch phase the produced ethanol and presumably other non-measured by-products such as organic acids were subsequently consumed. The ethanol concentration is almost constant between 10 h and 15 h. Nevertheless, biomass concentration increased exponentially but at a lower specific growth rate compared to growth on glucose. This means that another by-product was consumed during this phase.
with the set point value for the specific growth rate sp , the glucose feed concentration cS,f , and the value for the biomass concentration at feeding start cX,0 . The estimated values for the yield coefficient of glucose YX/S was 0.5 g g−1 and for the cellular maintenance mS was 0.1 g g−1 h−1 . The feedback part of the controller Fb is a standard PI algorithm:
Fb = KR ·
e+
1 · TN
e · d
(10)
with the control deviation e e = sp − app
3. Results
(8)
where F0 is the feed forward part (Riesenberg, 1991)
the integral time 30 min was used. The apparent specific growth rate used here was estimated from the heat flow measurements as described above. The controller gain was adapted automatically with increasing biomass concentration. The controller is started automatically after glucose limitation where the depletion of glucose is detected automatically by a sharp increase in the DO signal. To make sure that this increase is not only momentarily, the controller start is delayed by 30 min.
(11)
For the application to S. cerevisiae cultivations only the controller parameters (controller gain and integral time) had to be adjusted. The starting value for the proportional gain KR was 0.05 and for
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Fig. 3 compares the apparent and real specific growth rates during run A after feeding start. The real specific growth rate was determined from the dry cell weight data. The mean apparent specific growth rate app was 0.2 ± 0.03 h−1 . It is in good agreement with the mean value for the real specific growth rate which was 0.19 ± 0.03 h−1 .
3.2. Application of the calorimetric controller to cultivations of S. cerevisiae: feeding start after glucose depletion
Fig. 3. Comparison of apparent and real specific growth rate during a fed-batch cultivation of S. cerevisiae (run A). The apparent specific growth rate was controlled calorimetrically at a set point value of 0.2 h−1 after glucose and ethanol were depleted. The time profile of the measured biomass concentration is also shown.
Similar to this observation, biomass is still growing after ethanol is limited which supports the assumption that other by-products besides ethanol were produced during the batch phase. Possible by-products produced by S. cerevisiae are among others pyruvate, acetate and glycerol (Dijken et al., 1993; Postma et al., 1989). Heat production rate also increased exponentially to reach a value of 10 W l−1 . The estimated apparent specific growth rate fluctuates during this phase suggesting the successive consumption of different by-products produced from glucose during the preceding batch phase. This phase is finished after 22 h. The end is indicated by a sharp drop of the heat production rate to <1 W l−1 . The feeding was started automatically 30 min after this drop. At feeding start ethanol was depleted. During the first hour of the fed-batch phase only the feed-forward part of the controller was active to allow the system to settle. Then the feed-back part was switched on automatically. The set point for the specific growth rate was set to 0.2 h−1 which is less than the critical value of crit of approximately 0.3 h−1 (Woehrer and Roehr, 1981) to prevent the formation of by-products. Another reason for keeping the set point for the specific growth rate at moderate values is to prevent a limitation in the oxygen supply. To avoid oxygen limitation the reactor was operated temporarily at the maximum agitation rate of 1450 rpm, and the aeration gas was enriched with pure oxygen via mass flow controllers. At the beginning of the feeding the specific growth rate reached the set point value. The heat flow rate per unit volume reached a maximum value of 65 W l−1 at the end of the cultivation at 32 h and the dry cell weight concentration reached a final value of 55 g l−1 . During the fed-batch phase the glucose concentration was always lower than 0.5 g l−1 which is in the range of the critical value considering the measurement accuracy. No ethanol production was observed. The controller action is reflected in the time profile of the feed rate in Fig. 2A. The comparison of the RQ value with the cellular heat flow rate in Fig. 2C shows that after glucose limitation and before feeding start the RQ value is well below 1, indicating the consumption of ethanol. After feeding start when the specific growth rate is controlled below the critical value and no ethanol was produced, the RQ remains between 1 and 1.2, as expected. The major heat sinks and sources are shown in Fig. 2D. As seen from the data the most important heat flow is the heat flow to the reactor jacket followed by the heat generated by the stirrer. The heat flows due to gassing and the loss to the environment are of minor importance. The same is valid also for the heat flow by feeding solution which is therefore not included in the diagram.
During the second run B (Fig. 4) a different feeding strategy was used. While in run A the feeding was started after ethanol and presumably other by-products were exhausted, the feeding was started in run B shortly after the cells were glucose-limited after 8 h (Fig. 4A and B). The intention of this experiment was to show that the specific growth rate can be calorimetrically controlled with glucose feed even when ethanol is present. Initially, the set point for the specific growth rate was set to 0.25 h−1 . During the succeeding 8 h no ethanol was produced due to the limited supply of glucose. Moreover, ethanol and glucose were metabolized simultaneously by the cells and after 17 h ethanol was exhausted. The ethanol depletion is followed by a sharp decrease in the heat production rate and the apparent specific growth rate. The controller consequently increases the feed rate to compensate ethanol by glucose and to reach again the set-point value of the specific growth rate. The set-point was subsequently decreased to 0.18 h−1 to avoid a limitation in the oxygen supply. The controller was able to control the apparent specific growth rate to the set point values with a mean control deviation of approximately 0.05 h−1 (Fig. 4A). A final dry cell weight concentration of 110 g l−1 and a maximum heat flow rate of 85 W l−1 were achieved. Compared to run A the space-time yield was almost 3 times higher and reached a value of 4.5 g/(l h). The RQ value in Fig. 4C decreased gradually from 1.05 to 0.6 after feeding start until ethanol was exhausted. During the fed-batch phase the RQ was constant at a value of around 1.0. As in run A the heat flow to the reactor jacket is dominating the heat balance followed by the heat generated by the stirrer (Fig. 4D).
4. Discussion and conclusion The calorimetric controller which was developed for a recombinant E. coli strain in our previous work was successfully applied for HCDC of a S. cerevisiae strain in a standard bioreactor. The same calorimetric setup was used. Only the controller parameters had to be adjusted. During an HCDC the heat flow generated by the cells is high due to high cell densities. This makes the application of calorimetric methods advantageous. The sensitivity of calorimetric measurements increases with increasing biomass. At the beginning of the fermentation runs during the batch phase the cellular heat production rate is near the detection limit. However, during the fed-batch phase when the heat balance model is used to control the specific growth rate, the resolution of the heat production rate signal is sufficient for the control strategy. The apparent specific growth rate app was controlled at values smaller than the critical value crit , in order to avoid the Crabtree effect and therefore an unwanted production of growth-inhibitory by-products such as ethanol. Considering the measurement accuracy, the mean value of the apparent specific growth rate was in good agreement with the mean value of the real specific growth rate. This means that the specific growth rate could be estimated from the heat flow generated by the microorganisms. Since the cells remain in a constant metabolic state, no further measurements were necessary as input to the control scheme. However, attention has to be paid to the control of DO so that always an aerobic regime is guaranteed.
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Fig. 4. Time profiles of process parameters during a fed-batch cultivation of S. cerevisiae. The apparent specific growth rate was controlled calorimetrically after glucose was depleted after 8 h. The set point value for was 0.25 h−1 between 8 and 22 h and then changed to 0.18 h−1 A: heat production rate, feed rate and apparent specific growth rate; B: dry cell weight, glucose and ethanol; C: heat production rate and RQ; and D: major heat flows.
The apparent specific growth rate could be estimated using the cellular heat flow qp and the controller was able to maintain app reliably at the set point value. During process perturbations the apparent specific growth rate changed rapidly due to a sudden increase or decrease in the heat flow rate. A resulting negative value of the apparent specific growth rate is not necessarily an indicator of dying cells. In fact, rapid changes in heat flow and apparent specific growth rate reflect rapid changes in metabolism due to a change in the environmental conditions which may be the result of process perturbations. As a consequence of this fast response the controller can quickly adjust the feed rate such that the set point is reached again. When glucose is the only C-source in the medium during the fed-batch phase and no ethanol is produced, the RQ obtained from off-gas analysis is in the range of 1.1–1.2. The calorimetric control of the specific growth rate is in this case equivalent to the control of RQ. However, when ethanol and glucose are metabolized simultaneously, the RQ value drops continuously from 1.0 to 0.6 (Woehrer and Roehr, 1981). In this situation the calorimetric controller may have advantages over the RQ controller because the set-point of the specific growth rate is still constant while the RQ value is changing. If the RQ is controlled at a value between 1 and 1.2 ethanol is not formed but also not consumed (Woehrer and Roehr, 1981). Hence, the growth yield of calorimetric control is in this case higher since all the C-sources were used. Furthermore, the application of RQ data as sensitive control variable for S. cerevisiae fermentation requires a careful design (Shang et al., 2006). Another advantage of direct control of the specific growth rate is that the specification of the set-point may also consider the boundary conditions of a limited oxygen and heat transfer. The performance of the calorimetric control system is similar to traditional control strategies with respect to biomass concentrations achieved. van Hoek et al. (2000) attained 130 g l−1 after
48 h using an open loop control with an exponential feeding profile. Shang et al. (2006) used a RQ control strategy and gained 120 g l−1 after 65 h. Wang et al. (2010) applied a feedback strategy using an ethanol sensor and the DO value and reached 102 g l−1 after 38 h. With the calorimetric controller we achieved 110 g l−1 after 24 h. Certainly, further research needs to be carried out, especially in terms of improving the sensitivity of the calorimetric system in the standard bioreactor. Possible approaches include the improvement of temperature control, reduction of the jacket water circuit mass flow, optimization of controller tuning and enhancement of reactor insulation. A higher sensitivity of the calorimetric model would allow reducing the initial glucose concentration and therefore improve the calorimetric control performance. The calorimetric control strategy uses only easy measurable process parameters such as temperatures and flow rates. No complex and sensitive on-line analysis technologies are necessary. Therefore, the calorimetric method presents an effective alternative to the RQ control strategy from OUR or CER measurements via gas analysis. Hence, there is great potential for its application in industrial bioprocesses with standard bioreactors. Nomenclature
A CER ci cp DO e F g hv
heat exchanging area (m2 ) CO2 evolution rate (mmol l−1 h−1 ) concentration of component i (g l−1 ) specific heat capacity (J g−1 K−1 ) dissolved oxygen concentration (% air saturation) control deviation volumetric flow (l s−1 ) moisture content in gas (g g−1 ) enthalpy of vaporization of water (J kg−1 )
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m ˙ m ms n OUR q P Q RQ T U V Y
mass (g) mass flow (g s−1 ) substrate requirement for maintenance (g g−1 h−1 ) stirrer speed (rpm) oxygen uptake rate (mmol l−1 h−1 ) heat flow (W) electrical power (W) volumetric heat flow (W l−1 ) Respiratory quotient temperature (K) heat transfer coefficient (W m−2 K−1 ) volume (l) yield coefficient
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