A Laboratory Experiment for Teaching Bioprocess Control Part 2: Bioprocess Design, Modelling, Simulation, and Fermentation Execution

Proceedings of the 9th IFAC Symposium Advances in Control Education The International Federation of Automatic Control Nizhny Novgorod, Russia, June 19-21, 2012

A Laboratory Experiment for Teaching Bioprocess Control Part 2: Bioprocess Design, Modelling, Simulation, and Fermentation Execution Ruben Knapp*, Jan-Hinrich Rabe*, Sebastian Kirchgäßner*, Winfried Storhas*, Martin J. Wolf** 

* Mannheim University of Applied Sciences, Institute of Technical Microbiology, Mannheim, Germany (e-mail: [email protected], [email protected], [email protected], [email protected]) ** University of Mannheim, Mannheim, Germany (e-mail: [email protected]) Abstract: This second paper on the development of a biotech teaching experiment focuses on the bioprocess itself: After identification of requirements of a bioprocess to be suited for a laboratory experiment for students, the mathematical models of the chosen process are presented in detail for batch, fed-batch, and continuous mode of operation. Linearization of the nonlinear dynamics reveals the system neither to be completely controllable nor to be completely observable – with biological reasons and solutions to this problem given. For fed-batch operation, the capabilities of the Simulink simulation environment to simulate different feeding strategies are shortly depicted. With specially tailored learning materials (“from students for students”), mastering the differences between “nice” simulation data and real-world sensor signals is addressed. Some comments on the development of an adapted strain of the microorganisms to the medium as the basis for a successful fermentation execution conclude this paper. Keywords: Process Control, Biotechnology, Mathematical Model, System Analysis, Signal Conditioning. 

INTRODUCTION

2. FERMENTATION PROCESS

Beginning with a new masters program in biotechnology at the Mannheim University of Applied Sciences, succeeding the old German “Diplom” curriculum, the last author of this publication was given the opportunity to create, develop and teach 2 hours per week of a new mandatory 4-hours class on process automation (PRAE, where E denotes English language). This class was first taught in summer term, 2010, and been offered every term since.

In fermentation processes, microorganisms are grown to high cell densities while synthesizing a desired protein as product, which is used e.g. in chemical and/or pharmaceutical industries. In the process considered, the marine bacterium Vibrio natriegens is cultivated in a high salt medium with acetate as product and pyruvate as Carbon-source (C-source). This defined medium is based on a defined pyruvate medium (DPM) in a prior work (Berdalet, 1995) with additional trace elements. The fermentation is, in this first stage of development, only run in a batch operation (cf. sec. 2.2), however it is also mathematically modeled under fed-batch and continuous operation mode (sec. 2.3 and 2.4). In a batch, all educts are present in the fermenter from the beginning on; nothing is added later on except aeration. Thus, all state variables are changing continuously. Although only a very few properties like pH and temperature can truly be controlled in a batch, the batch operation mode forms the basis for understanding the process. Batch models are, in addition, easily extended for other modes of operation, and for observer design. Therefore the batch mode is examined exhaustively in this experiment. The nonlinear model is also linearized to enabling applicability of the basic linear system theory taught in the PRAE lecture.

To enable a deeper understanding of the students for the different views, problems, needs, and solution approaches of all the disciplines involved interactively in the automation of a bioprocess, a teaching experiment was developed, and is continuously refined, based on the contents of the PRAE lecture. It covers the complete automation of a process from modelling, systems analysis, controller synthesis, observer design, instrumentation, media preparation, sterilization, adaption of the microorganism to the defined medium (“strain development”), the performing of the actual fermentation itself, downstream-processing, sensory data conditioning, scale-up considerations, and even the final cleaning of the bioreactor. The complete hardware setting for the experiment was already described in a previous submission (Wolf et al., 2012). This paper now completely describes the bioprocess itself – a Vibrio natriegens fermentation – running (either in batch, fed-batch or continuous mode of operation) on the explained hardware, together with all didactical and laboratory requirements, benefits and pitfalls of such a bioprocess. An upcoming paper will provide an insight into the overall didactical concept of the experiment and into the palpable experiments’ instructions and will conclude this series of three papers. 978-3-902823-01-4/12/$20.00 © 2012 IFAC

The growth curve is typical for a batch growth on two substrates (diauxic growth), see Fig. 1. After a lag phase of around 1.5 hours, microorganisms start growing, and the substrate pyruvate is degraded until depletion after around 4 hours. Pyruvate is used for cell growth and cell division and is also degraded to the product acetate. Acetate itself is finally metabolized after pyruvate depletion which (only) for didactical reasons is an advantageous effect. In order to 384

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describe the process, the dissolved oxygen in the reactor is measured online by a Clark electrode. Also the total biomass is measured at-line via photometry and offline by drying and weighing. The substrate pyruvate and the product acetate are measured offline via chromatography (HPLC).

grown on MEG than on DPM: Cell density is measured by optical density; the MEG medium yielded maximum optical density. Nevertheless, DPM has crucial advantages compared to MEG being indispensible for the proper process description:  DPM offers the possibility to balance the substrate concentration of the C-source in an adequate way.  DPM nearly matches the absorption characteristics of water. Negative effects due to matrix interference are therefore limited compared to MEG. This is important for further process development with optical online monitoring of cell density or substrate and/or product concentration.  DPM produces less foam relative to MEG in low scale reactions. Fermentations yield the result that antifoam detergents are still needed, but can be reduced in amount if necessary.  Decreased use of antifoam detergents during fermentation has a positive effect on the oxygen mass transport in the gas-liquid interface. A higher concentration of the detergent polypropylen glycol (PPG) leads to a reduction of the kLa-value and therefore to a reduction of the oxygen transport to the cells (Morão et al., 1999).  The lower absorption of the DPM and the lower foaming tendency are crucial for a subsequent online measurement. A backscattering probe has lower matrix effects due to the lower absorption of the matrix, for example. The lower foaming tendency can improve pumping of the broth for an inline measurement with a sample loop.  Complex media have some general disadvantages: They differ in composition from charge to charge and are often not well-defined by available analytical methods. This fact in consequence complicates process control and monitoring, especially for student experiments.  The chosen state variables for system characterisation have to be measurable with the provided instruments.

Fig. 1. Metabolic activity and growth curve of a fermentation (Vibrio natriegens) represented by the state variables: Product (acetate) concentration, substrate (pyruvate) concentration, total biomass - bio dry mass as well as OD600 with a multiplication factor and oxygen concentration dissolved oxygen DO needed for the calculation of the concentration of oxygen cO2 in the liquid phase. 2.1 Requirements Analysis: A list of requirements had to be fulfilled before process development and analysis. These requirements are important for students to perform the experiments on their own or with minimal help. First, the fermentation medium and the microorganisms have to be adequate:  The high salinity of the medium prevents growth of almost all other microorganisms reducing the risk of biological contamination. This increases the manageability of the process in respect to students’ experience.  Although a S1-lab is available, using microorganisms that are not genetically modified increases safety of the process, esp. if problems occur during fermentation conduction.  Vibrio natriegens has a low generation time of 9.8 minutes when grown on a brain heart infusion (Eagon, 1962). This results in a relatively short fermentation time, as the experiment should be conductable within a single day, which is necessary for the teaching experiment. It is important because the focus should not be the fermentation itself but the monitoring and control behind it.  In contrast to the previous requirement, the costs for the fermentation should also be reasonable, which led to a compromise regarding media and thus execution time, see below.

2.2 Batch Fermentation Dynamics 2.2.1 Mathematical description of system balances The formation of vital biomass XV depends on the current concentration of the vital biomass XV(t) [g/l] and the growth rate µ [1/h] (1). Dead biomass XD(t) [g/l] is modeled similarly with a death rate KD [1/h] acting on the current vital biomass XV(t) (2). The overall biomass XO(t) [g/l] consists of the vital and dead biomass (3). The degradation of the substrate pyruvate is a function of the formed biomass which consumes substrate for product formation, cell growth, cell division and maintenance. Maintenance is the needed energy to sustain cellular processes like protein production or movement of cells. The maintenance factor MF [1/h] is embedded in the differential equation (4). µ1 [1/h] denotes the growth rate on the first substrate pyruvate. α [1/h] and β [-] are constants for biomass associated resp. growth associated product formation. The yield coefficients Y [-] are referring to biomass X, substrate S and product P.

Vibrio natriegens is also often cultivated on complex media like meat extract media with glycerin (MEG). At first glance, the complex media (MEG) has a lot of advantages compared to the defined pyruvate media (DPM). Vibrio natriegens achieves higher specific growth rates and lower lag times if

The product acetate is formed by enzymatic conversion of pyruvate, and it is also used as a substrate for cell growth and 385

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metabolic pathway is first suppressed as the substrate pyruvate is preferentially metabolized (10). KS,2 [g/l] and KO,2 [g/l] are as well saturation constants for pyruvate and oxygen. Ki [-] is a substrate inhibition constant weighted by b.

cell division after pyruvate depletion. Therefore, the formation and degradation of product is a function of the current biomass concentration XV(t) (5). µ2 [1/h] denotes the growth rate of the microorganisms on the second substrate acetate after the depletion of the primary substrate pyruvate.

(9)

(1)

(10)

(2) (3)

The death rate consists of three terms (11). The second and the third term describe the dependency of the death rate to the actual pyruvate or acetate concentration as the microorganisms die due to starvation.

(4)

(11)

(5)

OTR is characterized by the specific mass transfer coefficient kLa [1/h], the saturation concentration of O2 in the liquid phase cO2* [g/l] and its actual concentration of O2: cO2 (12).

Oxygen is balanced according to the two-film theory. The change of oxygen in the liquid phase is described by the oxygen transfer rate to the liquid phase, named OTR, and the transfer out of the liquid phase to the bacteria, named oxygen uptake rate OUR (6) with cO2 [g/l] denoting the oxygen concentration in the liquid phase.

(12) The cO2* concentration is calculated with the mean pressure [bar], the molecular weight of O2 [g/mol], the molar ratio of O2 in the air inlet [-] and the Henry coefficient of O2 in water [bar l/mol] (13). The dissolved oxygen DO [-] is measured with a Clark electrode during fermentation to calculate the actual oxygen concentration in the liquid phase cO2 (14).

(6) The lag phase at the beginning, where cells, after inoculation, adapt to the new environmental conditions, can be modeled by a control variable Q using a logistic function with the maximal specific growth rate on pyruvate µmax,1 [1/h], see (7). Q [-] is a quantity related to the physiological state of the cells (Baranyi, 1994).

(13)

(7)

(14) The oxygen uptake rate OUR is described by an empirical equation as a function of the current biomass concentration (Knapp, 2012b). The microorganisms consume oxygen for the growth on pyruvate and acetate as well as for the maintenance metabolism (15). The specific growth rate on pyruvate depends on the liquid volume VR,L [m³] with an exponent g0 [-] as a volume factor. OURF is a proportional factor [-].

2.2.2 Growth model and transport kinetics The growth phase of the microorganisms is modeled according to the classical Monod kinetics. Here, the overall growth rate split into the growth on the substrate pyruvate µ1, the growth on the product acetate µ2 and a death rate Kd expressing lack of available substrate (8). (8)

(15)

The growth on the substrate pyruvate µ1 is a function of the current concentration of the limiting substrates pyruvate and the oxygen dissolved in the liquid phase cO2 (9). KS,1 [g/l] and KO,1 [g/l] are saturation constants. The last term represents the lag-phase modeling with the variable Q. The initial value of Q is far below 1 and is rising. Therefore, it has a strong influence on µ1 at the beginning, but decreasing over time.

2.2.3 Parameter estimation The software tool Berkeley Madonna is used for parameter estimation by curve fitting. Herefore, the numerical integration method Runge/Kutta 4 is used, see Fig. 2. The overall biomass XO (yellow line) is fitted to the measured values of the bio dry mass in g/l (black data points), the substrate S (black line) is fitted to the measured pyruvate

Besides the growth on pyruvate, Vibrio natriegens also metabolizes the product acetate with a growth rate µ2. This 386

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concentration in g/l via HPLC (red data points), the product P (red line) is fitted to the measured acetate concentration in g/l via HPLC (green data points) and the dissolved oxygen DO (green line) is fitted to the data yielded from the pO2electrode (blue data points). The vital biomass (magenta line) decreases slightly after depletion of pyruvate and acetate. In contrast, dead biomass is formed due to starvation (cyan line). The fits of XO, S, P and DO show only low deviations to their respective datasets.

respective time points (cf. Table 1). As an example, the particular system matrix A for operating point 1 is given in equation (18). 2.2.5 Systems Analysis Controllability and observability are checked for the investigated fermentation process. To check controllability, a control matrix is calculated according to Kalman. For e.g. operating point 1, see (19), the system is proven not to be completely controllable. It is comprehensible due to the fact that XD and Q cannot be controlled independently of e.g. the vital biomass Xv. But, as controlling Q and the dead biomass XD can never be the goal of any process operation, examining only the remaining parts of the model is still valid.

2.2.4 Linearization In the case of the Vibrio natriegens fermentation the nonlinear trajectory of the dynamical system is determined by the six state variables XD(t), XV(t), S(t), P(t), cO2(t) and Q(t), as seen in equation (16). The system’s input is the oxygen input into the fermentation broth. The product concentration is the system’s output (17).

(19)

Therefore, the system order is reduced to n-2 (neglecting the state variables XD and Q); controllability is checked again and this model is proven to be completely controllable for all operating points.

(16)

Likewise, the observability matrix is also determined according to Kalman. The whole system is also proven to be not completely observable. Observability is also checked for the n-2 reduced system, and it is now proven to be completely observable.

(17)

Table 1. Operating points for fermentation F6 with the respective values of the state variables. Fermentation time Operating point

1h 1

3h 2

4.75 h 3

XD (t=tOP,j) XV (t=tOP,j) S (t=tOP,j) P (t=tOP,j) cO2 (t=tOP,j)

0.023 0.582 4.425 0.033 0.007 0.074

0.082 1.117 2.213 0.776 0.006 2.09

0.278 2.128 0.0 0.359 0.006 39.51

Q (t=tOP,j)

Fig. 2. Parameter estimation with multiple curve fits for the variables XO (yellow), Xv (magenta), S (black), P (red), DO (green), and XD (cyan). The nonlinear process is segmented into three linearized operating sections.

(18)

The design of an appropriate observer – maybe only for the “biomass state” variable (that is, so far, only measured offline) – can thus be applied as learnt in the PRAE lecture.

For linearization, the growth curve is divided into three operating sectors: A lag phase (from inoculation to 2 h), an exponential growth phase (from 2 h to 4 h), and the stationary phase (from 4 h to 5.5 h). One operating point is chosen for each section of the process: The three operating points are after 1 h, 3 h and 4.75 h fermentation time. The respective values of the state variables are read from the fitted Madonna Model, and are used for Jacobi-Matrix calculations at their

2.3 Fed-Batch Fermentation Besides the traditional batch fermentation, two more modes of operation exist: “fed-batch” and “continuous”. In fed-batch 387

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mode, the fermentation starts with only a fraction of the maximum possible reaction liquid volume being utilized, while the remaining volume is subsequently added as substrate feed over time. This is for example necessary, if high substrate concentrations are causing inhibitory effects on the cell metabolism.

either be constant, can increase linearly or exponentially. All these different operation modes can be simulated in the related Simulink simulation environment, see Fig. 3. Even though the exponential feed provides a convenient substrate supply for the exponentially growing cells, the main disadvantage of these feeding strategies is that they are not related to process parameters. For optimization, the feed can instead be adjusted to substrate (or biomass) concentration and reaction liquid volume. This prevents reactor overflow and maintains the substrate at an optimal concentration.

2.3.1 Dynamics: The differential equations of a fed-batch fermentation differ as follows compared to the batch equations: The volume over time V(t) can be described as the sum of the initial volume V0 and volume input V  t , see (20).

V (t )  Vo  V  t

2.4 Continuous Fermentations The differential equations of a continuous fermentation do not differ much from those of a fed-batch fermentation. In a continuous fermentation substrate is added over time, similar to a fed-batch fermentation. In addition, reaction medium is permanently drained to achieve constant concentrations in the reaction broth. This steady-state is reached, when dX/dt = 0 and dS/dt = 0. It allows running the process at optimal conditions over a long period of time with permanent harvesting of the product, and therefore optimizes the spacetime yield.

(20)

The increasing reactor volume results in a dilution rate D that depends on the ratio of volume input to current volume (21). D  V  / V

(21)

In general, substrate is fed (Sα) during a fed-batch fermentation, but no product or biomass. Therefore, the substrate concentration differs as described in equation (22). It has to be taken into account that feeding dilutes the medium. Thus the equations for biomass (23) and product (24) have to be adjusted accordingly:



dS X (t ) X (t )   1     1   M F X (t )  D S   S (t ) dt YX / S YX / P



(22)

dX  (   D) X (t ) dt

(23)

dP X (t )  (    1 ) X (t )   2  DP dt YX / P

(24)

In practice, a steady-state can be achieved in one of two ways: In case of a chemostat, the liquid-volume of the reactor remains constant due to constant volume inflow and outflow: a steady-state arises automatically. In a turbidostat, the biomass concentration is measured and kept constant by regulation of the flows. The high measurement and control effort of a turbidostat makes it almost irrelevant for industy. In a chemostat, the dilution rate D equals the growth rate µ when the steady-state is achieved. Nevertheless, the dilution should not exceed a critical value Dcrit, which equals the growth rate for the substrate inflow, see (28), to prevent wash-out of the culture:

Dcrit   max

2.3.2 Feeding Strategies

S ks  S 

(25)

Despite the high potential of a continuous fermentation, the required process knowledge is not sufficiently available for most industrial applications. Furthermore, the microorganisms’ genetic stability cannot be guarantied, and the possibility of mutation poses a liability for product loss. Finally, the entire process requires a high degree of sterility to prevent biological contamination, which may result in a complete loss. 3. SIMULATION DATA VS. REAL-WORLD SENSOR SIGNALS: SIGNAL CONDITIONING

Fig. 3. Simulink model of several feeding strategies for a fedbatch fermentation and their influence on volume (blue): Discontinuous/pulsed (grey), continuous/constant (orange), cont./linearily increasing (green) and cont./exponentially increasing (red).

According to a 2004 guideline of the U.S. Food and Drug Administration, new pharmaceutical processes should be developed using process analytical technologies (PAT). The aim is to enhance understanding, optimization and control of manufacturing processes yielding a higher quality of the pharmaceuticals produced (Food and Drug Administration, 2004). Using microorganisms or cell culture is one current key technology for innovative pharmaceuticals. In this

The increase of volume and substrate by feeding normally is one of the critical design decisions: The feed can e.g. be applied discontinuously; here substrate is pulse-fed at regular intervals. Continuous feed is another strategy: The inflow can 388

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respect, the implementation of new process analytical technologies and enhanced process control can help to fulfill the requirements of the Good Manufacturing Practice (GMP) required by the PAT initiative with regards to quality as well as process documentation and control.

10 to approx. 7-8 hours. The cell densities in the experimental series, on the other hand, were only raised by 10 %.

For biotechnology students the pharmaceutical industry is one important prospective field of occupation. With respect to GMP demands it is beneficial thus not only to understand the functional principles, advantages and pitfalls of common measurement methods or upcoming technologies like online spectroscopy, but also to be familiar with sensor signal generation, transmission, sampling, reconstruction and interpretation of measurement data – as scientists often face difficulties due to biological variation or impurities of raw materials, final products or even microorganisms that cause interference of measurement signals. Also, data may be distorted in large production sites, thus meaningful data may become mixed with insignificant “noise”. Disturbing quantities and interfering signals complicate obtaining data adequate for process documentation and automation. Therefore, in some cases it is advisable to process sensor signals before using them for fermentation control or visualization to avoid errors.

The authors would like to thank Dipl.-Ing. (FH) Michael Reuter, Mannheim University of Applied Sciences, and Dipl.-Ing. Frank Stolzenberger, Heidelberg University, for their professional and dedicated technical support, as well as Derek Rhys, University of Washington, and Silke Weiß, HDZ, Freiburg University, for proofreading and improving this manuscript.

ACKNOWLEDGMENTS

REFERENCES Baranyi J., Roberts T. A. (1994). A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology, (23), 280. Berdalet E., Packard T., Lagacé B., Roy S., St-Amand L., Gagné J.-P. (1995). CO2 production, O2 consumption and isocitrate dehydrogenase in the marine bacterium Vibrio natriegens. AQUATIC MICROBIAL ECOLOGY., (9), 211-217. Eagon, R. G. (1962). Pseudomonas natriegens, a marine bacterium with a generation time of less than 10 minutes. Journal of Bacteriology, (83), 736. Food and Drug Administration (2004). Guidance for Industry PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. Food and Drug Administration, Rockville, MD, USA. Kirchgäßner, S. (2011). Signal conditioning for fermentation processes. Lab Project, Mannheim University of Applied Sciences. Knapp, R. (2012 a). Performance Analysis of Selected Defined Media for a Vibrio natriegens Fermentation. Lab Project, Mannheim University of Applied Sciences. Knapp, R. (2012 b). Model-based development of a fermentation process, including system analysis, used as a teaching experiment in process automation. Master’s Thesis, Mannheim University of Applied Sciences. Morão A., Maia C. I., Fonseca M. M. R., Vasconcelos J. M. T., Alves S. S. (1999). Effect of antifoam addition on gas-liquid mass transfer in stirred fermenters., Bioprocess Engineering, (20), 165-172. Seidel, G. (1999). Kultivierungen mit einem Hochleistungsstamm von Acremonium chrysogenum in komplexen und synthetischen Medien – Strategien zur Produktivitätssteigerung unter Berücksichtigung der Enzymaktivitäten der Cephalosporin C-Biosynthese, Dissertation, Universität Hannover. Storhas, W. (1994)., Bioreaktoren und periphere & Sohn Einrichtungen. Friedrich Vieweg Verlagsgesellschaft mbH, Braunschweig/Wiesbaden. Wolf, M.J., Ninov, V., Babel, H., Hütter, K., Staudt, R., Storhas, and Badreddin, E. (2012). A Laboratory Experiment for Teaching Bioprocess Control – Part 1: Hardware Setting. Accepted for publication at the 9th IFAC Symposium on Advances in Control Education 2012, Nizhny Novgorod, Russia.

To fulfill this requirement, learning material covering a broad overview of fundamental concepts of signal processing, including the characteristics of different filter types, as well as filter design and application in Simulink, is provided to the students by the end of the semester for further self-regulated learning. In order to ensure a high degree of accessibility the learning material was developed in a cooperative process with a former student (Kirchgäßner, 2011), taking into account the perspective of a learner who has attended the course and reached the same level of knowledge about process automation. Accordingly, students are then able to deepen their understanding of the subject, to reconstruct and apply the examples provided in the additional learning material and thus to acquire further crucial skills in the design, implementation, deployment and verification of analog and digital filters for basic signal conditioning applications on their own. Whereas the process of developing the learning material has almost been completed, the learning tasks to be implemented in the experiment are still work in progress. 4. PREREQUISITES: ADAPTION OF VIBRIO NATRIEGENS TO DEFINED MEDIUM DPM The microorganisms are adapted to the culture medium DPM over multiple generations in the course of five shaking flask experiments (Knapp, 2012a). This allows the microorganisms to be screened for increasing production of appropriate metabolic enzymes. These experiments are conducted under equal conditions meaning nearly equal inoculation density (equal biomass concentration at the beginning of the experiment). Additionally, the bacterial culture is always about in the same metabolic state of the growth curve. By these experiments the lag time was reduced from 4 to approximately 3 hours. The time for reaching the stationary phase (see operating section 3 in Fig. 2) was shortened from 389