storage metabolism in Saccharomyces cerevisiae:
novel minibioreactors and
13
C studies
Proefschrift
ter verkrijging van de graad van doctor
aan de Technische Universiteit Delft,
op gezag, van de Rector Magnificus, Prof. dr. ir. J.T. Fokkema,
voorzitter van het College voor Promoties,
in het openbaar te verdedigen op dinsdag 25 november 2008 om 15:00 uur
door
Fredrick Otieno ABOKA
Scheikundig ingenieur
geboren te Siaya, Kenia
Prof. dr. ir. J.J. Heijnen
Samenstelling promotiecommissie:
Rector Magnificus voorzitter
Prof. dr. ir. J.J. Heijnen Technische Universiteit Delft, promotor Prof. dr. J.T. Pronk Technische Universiteit Delft
Prof. Dr.-Ing. Dr. h.c. M. Reuss Universität Stuttgart, Duitsland
Prof. J.M. François Institut National des Sciences Appliquées, Toulouse, Frankrijk
Prof. dr. ir. M.C.M. van Loosdrecht Technische Universiteit Delft Dr. ir. W.A. van Winden DSM Anti-Infectives, Delft
Ir. A. Oudshoorn Applikon Biotechnology BV, Schiedam
Prof. dr. S. de Vries Technische Universiteit Delft, reservelid
The studies presented in this thesis were performed at the Bioprocess Technology section, Department of Biotechnology, Delft University of Technology. The research was financially supported by the Dutch Science Foundation (NWO), the Dutch Ministry of Economic Affairs, Applikon Biotechnology BV and DSM NV.
For the things we have to learn before we can do them, we learn by doing them
5
Summary
Stimulus response experiments are useful for generating quantitative biological data needed for in vivo kinetic modeling of cellular metabolism. Kinetic modeling of cellular metabolism offers the potential to deepen our understanding of non linear complex cellular metabolism and could ultimately lead to a more rational basis for genetic modification of cellular metabolism with significant potentials for economic, health and environmental benefits.
The classical approach for stimulus response experiments is to perturb a microbial culture by introducing a stimulus in a laboratory scale bioreactor. Introducing a stimulus into a bioreactor is not ideal because the perturbed culture may not be immediately available for additional stimulus response studies. The required high frequency sampling for fast dynamics is a major challenge which may only be overcome with expensive automation. The Bioscope (Visser et al., 2002: Mashego et al., 2005) is an elegant answer to the mentioned challenges of stimulus response experiments. The Bioscope by Visser et al. and Mashego et al. - from here on referred to as Bioscope II to distinguish it from Bioscope III developed in this work - is plug flow devices used perform pulse type perturbations of whole cells and to monitor the response of the cells to the perturbations. Although very useful as a means for excitation of a metabolic system, a pulse results in a cellular uptake rate that is controlled by the cells themselves. It is also highly desirable that the stimulus response experimentalist be able to control the substrate uptake rate of the cells at a preferred level, as is usually the case in a conventional chemostat or fed batch operation. That possibility can be brought about with step response experiments.
The research work presented in this thesis set out to develop an experimental device for step response experiments. The challenge was that the new device(s) should mirror the strong advantages that Bioscope II already brings to the arena of stimulus response experiments. In the course of this work, two devices (Biocurve and Bioscope III) were developed and their prototypes characterized (with respect to mass transfer, mixing and pressure drop) and validated by using them to perform biological experiments
The Biocurve is a miniature chemostat for step perturbation experiments. Chapter 2 describes the design of the Biocurve. The number of transient data points that can be generated using the Biocurve is, however, strongly limited by the small biomass volume. Still, the robustness of the online measurements such as dissolved oxygen and carbon dioxide concentrations that can be obtained with the Biocurve system opens other opportunities for application of the Biocurve concept as for example, a screening device for rapidly identifying informative stimulus response
6 experiment. In order to be useful, a stimulus response experiment should produce a quantitatively and accurately measurable response in terms of changes in intracellular metabolite concentrations. Chapter 3 presents the successful identification of a step stimulus with an unexpected strong response, followed by the in depth investigation of this stimulus in the fermenter (chapters 3 & 5).
Bioscope III, like its progenitor, Bioscope II, is a satellite plug flow device that allows the same bioreactor culture to be used for multiple stimulus response experiments. Bioscope III is specifically designed to enable application of step stimuli. In this novel device, the substrate is supplied to the cells in the broth channel by diffusion from a feed channel, over a dialysis membrane which separates the feed from the broth channel. In chapter 4, a prototype of Bioscope III was successfully characterized and the validity of this new concept adequately demonstrated with biological experiments.
The informative stimulus response experiment identified with the Biocurve in chapter 3 was a modest 50% step increase in glucose supply rate to a glucose limited aerobic chemostat culture of S. cerevisiae (D = 0.05h-1). This rather modest perturbation caused unexpected transient overshoot in oxygen uptake rate. The perturbation experiment was repeated in the conventional 4.0L lab scale bioreactor confirming the surprising response. In the fermenter experiment, the oxygen uptake rate increased from approximately 40 to 100 mmol/C-mol biomass/hr (nearly 3-fold) within 15 minutes before slowly decreasing to its expected new steady state value. The strong response suggested a rapid mobilization of storage carbohydrates. Indeed, measurements in the same period confirmed that the storage carbon (glycogen and trehalose combined) content of biomass decreased by approximately 64% before slowly increasing to a new level. This observed storage mobilization (chapter 3) has enormous implications for kinetic interpretation of the short term (<300 sec) intracellular fluxes in pulse or step response experiments.
The storage carbon mobilization is revisited in chapter 5 for more detailed study using an approach in which a glucose limited aerobic chemostat culture of S. cerevisiae (D = 0.05h-1) was subjected to switches from unlabeled to 13C-labeled medium supply both without and with shift in the dilution rate. The 13C-labeling of glycolytic intermediates and 13CO2 in the offgas were monitored. The difference between the transient 13C-labeling patterns of the various metabolites and CO2 measured without and with the shift in dilution rate clearly, and independently confirmed the mobilization of unlabeled storage pools shortly after the step change. The outcome of this novel type of dynamic 13C labeling studies shows that for correct calculation of intracellular fluxes in dynamic experiments with yeast, the storage carbon metabolism, including turnover at balanced growth, needs to be considered very seriously.
7
Samenvatting
Stimulus response experimenten vormen een belangrijke bron van kwantitatieve biologische data die gebruikt kunnen worden om het celmetabolisme kinetisch te modelleren. Dergelijke modellen verschaffen ons inzicht in het complexe, niet lineaire gedrag van het celmetabolisme en kunnen op termijn de basis vormen voor rationele genetische aanpassingen van het metabolisme. Dit laatste biedt aanzienlijke perspectieven op het gebied van winstgevendheid van (bio)processen, welzijn van de mens en behoud van ons milieu.
Conventionele stimulus response experimenten bestaan uit het verstoren van een microbiële cultuur door een stimulus toe te dienen aan een laboratorium schaal bioreactor. Een nadeel van deze aanpak is dat de eenmaal verstoorde cultuur niet onmiddellijk beschikbaar is voor verdere stimulus response studies. De vereiste hoge frequentie van monstername is een andere uitdaging, die kostbare automatisering noodzakelijk maakt. De Bioscope (Visser et al., 2002: Mashego et al., 2005) biedt een elegante oplossing voor de bovengenoemde uitdagingen bij stimulus response experimenten. De Bioscope van Visser et al. en Mashego et al. – vanaf nu Bioscope II, ter onderscheiding van Bioscope III die werd ontwikkeld in dit promotieonderzoek – is een propstroomreactor die specifiek is ontworpen om het effect van verstoringen op de cel te onderzoeken. Hoewel de toediening van een puls substraat zeer bruikbaar is om een metabool systeem te verstoren, leidt dit tot een nieuwe substraat opnamesnelheid die door de celeigenschappen is vastgelegd. Het zou de voorkeur verdienen als de onderzoeker zelf de substraat opnamesnelheid op het beoogde niveau kan vastleggen, zoals dat ook in een chemostaat of fed batch cultuur het geval is. Dit zou het gebruik van step response experimenten mogelijk maken.
Het onderzoek dat in dit proefschrift is beschreven had tot doel om een experimenteel gereedschap te ontwikkelen voor step response experimenten. Randvoorwaarden waren dat het ontwerp de experimentele voordelen van de Bioscope II zou behouden. Er werden twee verschillende experimentele gereedschappen ontworpen: de Biocurve en de Bioscope III. De prototypes hiervan werden onderzocht op hun stofoverdracht, menggedrag en drukval en werden vervolgens getest in biologische experimenten.
De Biocurve is een mini-chemostaat voor het uitvoeren van stapvormige verstoringen op een celcultuur. Hoofdstuk 2 beschrijft het ontwerp van de Biocurve. Het kleine biomassa volume in de Biocurve vormt een sterke beperking van het aantal monsters (datapunten) dat met dit experimentele gereedschap gegenereerd kan worden. Het nut van de Biocurve schuilt in online metingen (b.v. DO2, DCO2) die ermee uitgevoerd kunnen worden. Deze maken het een geschikt gereedschap om snel
8 veel verschillende verstoringen te testen en de meest informatieve te identificeren. Informatief zijn verstoringen die leiden tot een nauwkeurig te kwantificeren verandering van intracellulaire metabolietconcentraties. In hoofdstuk 3 wordt een succesvolle toepassing van de Biocurve beschreven, waarin een stapvormige verstoring werd geïdentificeerd die leidde tot een onverwacht sterk effect op het celmetabolisme van bakkergist. Vanwege de beperkte mogelijkheid tot het nemen van monsters uit de Biocurve, werd het effect van deze verstoring vervolgens in detail bestudeerd met behulp van eenzelfde verstoring in een conventionele chemostaatcultuur (hoofdstukken 3 en 5).
Net als zijn voorganger, de Bioscope II, is ook de Bioscope III een propstroomreactor die extern aan een bioreactor wordt gekoppeld en het mogelijk maakt om dezelfde bioreactor cultuur meerdere malen te gebruiken voor een scala aan verschillende stimulus response experimenten. Bioscope III werd specifiek ontworpen om stapvormige verstoringen mogelijk te maken. In dit nieuwe experimentele gereedschap wordt het substraat aan de biomassa in het kanaal met fermentatiebeslag toegediend via diffusie vanuit een parallel kanaal waardoor voeding stroomt. Beide kanalen worden gescheiden door een dialysemembraan. Hoofdstuk 4 beschrijft de succesvolle karakterisering van het prototype van de Bioscope III en biologische experimenten die ermee werden uitgevoerd om het ontwerp te valideren.
De informatieve stapvormige verstoring die in hoofdstuk 3 werd beschreven was een bescheiden 50% verhoging van de snelheid waarmee glucose werd toegediend aan een glucose gelimiteerde aërobe chemostaatcultuur van S. cerevisiae (D = 0.05h-1). De verstoring leidde tot een tijdelijke overshoot van de zuurstof opnamesnelheid. Het experiment werd succesvol herhaald in een 4.0 L schaal laboratorium bioreactor. In dit laatste experiment nam binnen 15 minuten de zuurstofopnamesnelheid toe van 40 naar 100 mmol/C-mol biomassa/uur (bijna een verdrievoudiging), om vervolgens langzaam terug te keren naar de verwachte steady state waarde. Deze sterke reactie suggereert dat opslagkoolhydraten worden aangesproken. Monsters die tijdens dezelfde tijdspannen genomen waren bevestigden dat de cellulaire fractie aan opslagkoolhydraten (glycogeen plus trehalose) met ongeveer 64% afnam, en daarna weer toenam naar een nieuw stabiel niveau. Dit aanspreken van de koolhydraatvoorraad van de cel (hoofdstuk 3) heeft grote consequenties voor de kinetische interpretatie van de intracellulaire metabole fluxen die kort (<300 seconden) na het toedienen van een puls- of stapvormige verstoring worden waargenomen.
Hoofdstuk 5 behandelt een verdere studie naar het aanspreken van de koolhydraatvoorraad van de cel na een stapvormige verstoring van de snelheid van de glucose toevoer. In een tweetal experimenten werd ongelabeld medium van een glucose gelimiteerde aërobe chemostaatcultuur van S. cerevisiae (D = 0.05h-1)
9 omgewisseld voor 13C-gelabeld medium, waarbij in het ene experiment de glucose toevoer snelheid ongewijzigd werd gelaten, en in het andere experiment de glucose toevoer snelheid werd verhoogd. De 13C-labeling van intermediairen van de glycolyse en van CO2 in het afgas van de fermentatie werd gemeten. Het verschil tussen de ontwikkeling van de 13C-labeling van de genoemde metabolieten in beide experimenten vormt een duidelijke, onafhankelijke, bevestiging van het aanspreken van de koolhydraatvoorraden kort na de stapvormige verstoring. De uitkomsten van dit nieuwe type van dynamische 13C-experimenten toont aan dat er bij het berekenen van intracellulaire fluxen in dynamische experimenten met gist serieus rekening gehouden dient te worden met het metabolisme van glycogeen en trehalose, inclusief de turnover van deze voorraden tijdens gebalanceerde groei.
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Contents
Summary 5
Samenvatting 7
Chapter 1
General Introduction 11Chapter 2
Characterization of an experimental miniature bioreactor for cellular perturbation studies19
Chapter 3
Identification of informative metabolic responses using a minibioreactor: a small step change in the glucose supply rate creates a large metabolic response in Saccharomyces cerevisiae43
Chapter 4
Characterization and validation of Bioscope III: A new experimental tool for step response perturbation experiments67
Chapter 5
Dynamic 13C-tracer study of storage carbohydrate pools in aerobic glucose limited Saccharomyces cerevisiae confirms a rapid steady state turnover and fast mobilization during a modest step up in glucose uptake rate101
Chapter 6
Future Directions 119References 121
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Chapter 1
General Introduction
Industrial microbiology (past, present and future)
Fermentation has been practiced by humans long before they had any knowledge of the existence of the micro organisms involved. In the context of modern industrial microbiology, the word ‘fermentation’ is generally applied to ‘any process for the production of a product by means of mass culture of micro-organisms’ (Stanbury et al., 1984). Micro-organisms are biochemically the most diverse group of organisms on earth. They represent a staggering repertoire of biochemical conversion capabilities that arguably makes them the most powerful and, in some ways, irreplaceable agents for efficient transformation of simple substrates into compounds that are needed by humans and for degrading undesirable complex compounds into harmless ones.
Today, only a handful of ‘industrial’ microorganisms such as Saccharomyces cerevisiae and Escherichia coli are being used in different fermentation processes to produce an increasing variety of useful products on top of the traditional bread, beer and wine (Gottschalk, 1986; Walker, 1998; Van der Werf, 2005). The growing list of products includes enzymes, amino acids, vaccines, antibacterial agents, antifungal agents, therapeutic proteins, monoclonal antibodies, vitamins, flavoring agents, lipids, biopolymers, biopesticides and microbial cells themselves (e.g. baker’s yeast). The genetic diversity of micro-organisms in nature combined with the versatility of the biochemical transformations suggests that there still exists a wealth of untapped resources of biochemical nature (Marwick et al., 1999). In addition, recent developments in molecular biology and genetic engineering are fuelling expectations for new fermentation processes and products as well as dramatic improvements of existing ones through the use of modified industrial microorganisms.
Development and optimization of industrial fermentation processes
The first steps in the development of a fermentation process in the pharmaceutical or chemical industry involve identification of a biological system or micro-organism capable of producing a desired (novel) biochemical compound and a search for optimal cultivation conditions (Puskeiler et al., 2004). The number of potential biological systems and novel pathways for biologically produced compounds has
12 increased rapidly due to the effort put into sequencing of unknown isolated genes and entire genomes of microorganisms (Liolios et al., 2007). The identified micro-organisms usually undergo further laboratory scale experiments to investigate various aspects such as optimal cultivation media and physicochemical properties in so far as these relate to the product yield, quality and biosynthesis rate. In addition, experimental data on the mass transfer and shear stress are needed to provide the fermenter design requirements and for optimal bioprocess operation at the industrial production scale. Finally, the recovery and purity of the desired product or downstream processing is an important element that must also be carefully considered at the research and development phase.
Significant improvements of industrial fermentations can be realized by optimizing bioprocess parameters. However, it is also possible to develop and optimize bioprocesses by improvement of the performance of industrial microorganisms through metabolic engineering (Bailey, 1991; Cameron and Tong, 1993; Stephanopoulos et al., 1998). Metabolic properties of industrial microorganisms are widely investigated with a motivation of a directed improvement of product formation or cellular properties through the modification of specific biochemical reaction(s) using recombinant DNA technology. Such targeted alteration of metabolic pathways allows the redirection of metabolic fluxes towards desired final products.
While some successes have been reported (see e.g. Stephanopoulos and Stafford, 2002 and references therein) the potential benefits of applying metabolic engineering methods are far from fully realized. Advances in this arena are delayed by the lack of insight in the complex regulation of the cell that often counteracts intended modifications. A deliberate approach that carefully evaluates available potential is needed to find the best way for modifying industrial strains and help turn the promise of industrial biotechnology into profitable applications.
Metabolic Engineering Tools
Metabolic Engineering (ME) is a multidisciplinary approach relying on several tools in various disciplines. Many researchers in ME agree that a kinetic model is a necessary tool because of its predictive power. Kinetic models can therefore provide
the much needed foundation for successful targeted alteration of cellular metabolism
(Bailey 1991; Kitano, 2002; Stephanopoulos et al., 2004; Wang and Hatzimanikatis, 2006; Bailey, 1998; Gombert and Nielsen, 2000; Visser et al., 2003; Heijnen et al., 2004; Heijnen, 2005; Reuss et al., 2007). The building of kinetic models requires quantitative in vivo experimental data of cellular metabolism obtained with whole cells (Wright et al., 1992; Teusink et al., 2000). This thesis addresses the need for
13
efficient and reproducible approaches for generating experimental biological data through application of a stimulus to whole cells and measurement of the resulting transient response.
Stimulus response strategy for kinetic modeling of cellular
metabolism
Stimulus response strategies for generating quantitative in vivo biological data exploit the plasticity of cells, that is, ‘the cell’s ability to change its biochemical make up upon an external or internal perturbation’ (Snoep, 2000). The general strategy entails:
1. Cultivation of microbial cells of interest in a well defined and reproducible physiological reference state. Continuous cultivation (chemostat) is most preferred unless the bioprocess has certain features, e.g. instability of recombinant cells that preclude long term continuous operation, in which case, either a fed-batch or batch has to be used. When employing a chemostat to set the reference steady state, one should bear in mind that a steady state with respect to metabolic fluxes does not exclude the possibility of significant changes in metabolite concentrations and enzyme levels during long term operation of chemostats as reported by Mashego et al. (2005) and Jansen et al. (2005),
2. The external cellular environment of the reference culture is then deliberately perturbed by introducing a stimulus in the form of a pulse or step. In case of a chemostat reference culture, the stimulus is often, but not necessarily, the limiting substrate,
3. The metabolic response of the cells is accurately measured. This involves i. rapid sampling (steady state and during transient)
ii. quenching of metabolic reactions
iii. separation of cells and culture supernatant
iv. extraction and concentration of intracellular metabolites v. analysis of metabolites
4. Identification of the kinetics of the biochemical reactions needs to be realized from transient data leading to a kinetic model based on in vivo kinetic properties. This involves first building a kinetic model with unknown parameters and then estimating all parameters, which is essentially an optimization problem whereby the required parameters are iteratively adjusted to obtain an optimal ‘fit’ between the model’s output and the empirical data. Parameter estimation is an established field of science and has received extensive coverage in scientific literature. The kinetic model can be used to
14 design subsequent rounds of informative experiments thus take the process back to step 1. The iteration continues until the desired quality of data and model is obtained.
The scope of this thesis is limited to the development of perturbation strategies and rapid sampling devices for fast (<300s) stimulus step response studies of microbial systems (steps 2 and 3). Such devices must allow rapid, reproducible and aseptic withdrawal of samples from the perturbed fermenter culture. The withdrawn samples must be immediately quenched in order to accurately represent the cellular state at the time of sampling. The time interval between sampling and subsequent quenching must be much shorter than the turnover time of the metabolites being measured (Weibel et al., 1974). Extraction of metabolites must be carefully designed and performed to minimize losses of extracted metabolites through degradation and to minimize factors such as the presence of salts and solvents in the extracted sample that can impact subsequent analysis.
One of the earliest comprehensive descriptions of rapid sampling can be found in the proceedings of a symposium organized under the auspices of the International Union of Biochemistry to discuss and evaluate the then available ‘apparatuses for rapid mixing and sampling, and to point to the limitations which nature sets to their performance and to those which technology can extend or overcome’ (Chance et al., 1964). A majority of the sampling techniques that were discussed allowed periodic batch wise removal of aliquots from the reaction vessel at defined time intervals (Lonberg-Holm, 1964; Miettinen, 1964). A decade later, Weibel et al. (1974) proposed a rapid sampling technique based on pressurization to quickly force aliquots out of the fermenter. Sustained interest in the development of robust and accurate rapid sampling methods has led to a large number of related articles since the 1990s (Theobald et al., 1993, Hartbrich et al., 1996; Schaefer et al., 1999; Lange et al., 2001; Buziol et al., 2002; Visser et al., 2002; Mashego et al., 2006).
The rapid sampling techniques that have been described in literature are broadly based on two approaches for stimulus response experiments: excitation of the cells inside or outside the fermenter. In the first approach, a reference culture in a fermenter is stimulated by imposing a pulse or a step change on the extracellular environment of the cells. After the perturbation, aliquots of the fermenter culture are withdrawn periodically in a batch wise manner within a time window of milliseconds to a few minutes after the perturbation. This time window is necessary to capture regulation at the metabolic level and not at the levels of DNA transcription and mRNA translation that can be considered to be in a frozen state (Kresnowati et al., 2007). The significance of this is that biologically meaningful kinetic information can be derived from metabolite measurements alone. This is an important advantage
15 because it eliminates the need for laborious transcriptome and proteome quantification throughout the transient.
An innovative technique for rapid sampling of a perturbed fermenter culture was presented by the research group of Reuss in Stuttgart, Germany (Theobald et al., 1993). The device consisted of a valve system and pre-cooled vacuum sealed glass tubes. The system developed by Theobald et al. was further developed by the research group of Heijnen in Delft, The Netherlands (Lange et al., 2001) who eliminated the potential risk of sample contamination by stagnant broth in a dead volume as well as replacing evacuated test tubes which were liable to premature vacuum loss by introducing a more robust system. Schafer et al. (1999) presented an automated rapid sampling system that improved the precision, accuracy and frequency of sampling of a perturbed fermenter.
The stimulus response strategy whereby the perturbation event is initiated inside the fermenter has the disadvantage that once disturbed, the fermenter culture may not be immediately used for additional stimulus response experiments. This impedes applying multiple perturbations to a culture in the same reference state, especially when the sampled culture is a (fed) batch. Furthermore, the high frequency of sampling which is needed for measurement of transient metabolic responses is cumbersome without expensive automation.
The alternative strategy for stimulus response experiments outside the fermenter has its origin in the stopped-flow concept for fast measurements of enzymatic reactions. In this strategy, a continuous stream of the fermenter culture is supplied to a smaller reactor where it mixes with a continuous stream of the perturbation agent. The mixture of cell suspension and perturbing agent continues to flow as a ‘plug’along a tube from where it can be sampled directly into a quenching solution after a suitable time delay.
The sampling apparatuses in this category are typified by the stopped-flow sampling technique (Buziol et al., 2002) and the Bioscope (Visser et al., 2002; Mashego et al., 2006). The fact that the cells are perturbed outside the fermenter means that the same fermenter culture can supply broth for more than one stimulus response experiment. Besides the efficient use of the fermenter culture, using the same reference culture for multiple stimulus response experiments yields perturbation data of higher quality for kinetic studies. Also, the sampling times and the reaction times in these systems are completely uncoupled (the reaction time is fixed by residence time or distance from the mixing point) which enables taking samples of any desired volume plus precise time resolution of samples even at extreme sampling frequencies and the extremely short time of the first sample (e.g. 100ms) after the perturbation (Buziol et al., 2002). Assuming that quenching is instantaneous and that there is plug flow, the time resolution is only limited by the time required by the
16 mixture to flow from the point of mixing to the sampling point. Unlike the stopped-flow sampling device developed by Buziol et al., the Bioscopes have gas and broth channels that are separated by a silicone membrane for oxygen and carbon dioxide exchange, a feature that allows overcoming oxygen limitation problems for aerobic cultures.
The ‘stimulus’ in a stimulus response experiment can be applied as pulse or step leading to pulse or step response data respectively. The Bioscope: a Biosystem for Continuous Pulse Experiments (Visser et al., 2002, Mashego et al., 2005) is a novel sampling device for pulse experiments. In pulse experiments, the uptake rate of the perturbing agent is largely determined by the uptake and the metabolizing capacity of the microbial cells being studied. This severely limits both the number of different perturbations that can be applied to a culture, and the possibility to study the effects of small metabolic stimuli on cells. In contrast, step response experiments allow the experimenter to control the specific uptake rate of the stimulus by the cells just like in conventional chemostat or fed-batch operations. It has been suggested (Nikerel et al., 2006) that the parameter identifiability problems arising from the large number of parameters in kinetic models may be overcome by using enriched data sets from independent perturbation experiments. In this context, the significance of step response experiments to generate richer data sets for kinetic parameter estimation is obvious.
Scope and outline of this thesis
The main goal of the research presented in this thesis was to develop a new device for step response experiments. An important requirement was that the new device should embrace the significant advantages that the pulse Bioscope brings to stimulus response experiments. To this end, two novel milliliter scale bioreactors to be used outside the reference fermenter, the Biocurve (Chapter 2) and Bioscope III (Chapter 4) have been developed and their prototypes characterized and validated.
The Biocurve is a well mixed minibioreactor for step response experiments. Liquid mixing in the Biocurve is achieved by a high recycle flow of broth. Chapter 2 presents characterization of the Biocurve with respect to liquid phase mixing and oxygen and carbon dioxide mass transfer. Biological experiments using a steady state aerobic culture of S. cerevisiae confirmed the accuracy of the Biocurve characterization.
The Biocurve is equipped with online sensors for oxygen and carbon dioxide enabling to rapidly evaluate cellular responses to several different perturbations of their environment. For this reason, the Biocurve can also be used as a screening tool
17 to identify informative stimulus response experiments for further study in a conventional bioreactor which allows more detailed analysis of the metabolic responses to the applied stimulus. The Biocurve identified an unexpectedly informative perturbation where the glucose uptake rate was increased by only 50% in a glucose limited aerobic chemostat culture (D = 0.05h-1) of S. cerevisiae. Chapter 3 provides an in depth analysis of this perturbation, which shows the importance of the rate of storage carbohydrate metabolism. From the in vivo kinetic point of view, the relevant result is that the glycolytic flux is completely uncoupled from uptake rate, showing that quantification of storage mobilization is absolutely essential for accurate elucidation of the kinetic parameters of glycolytic enzymes.
The second minibioreactor resulting from this work is Bioscope III (chapter 4). As its name suggests, this new device is similar to the pulse response Bioscopes prototyped by Visser et al. (2002) (Bioscope I) and further developed by Mashego et al. (2005) (Bioscope II). In Bioscope III the broth channel is not only aerated by a parallel gas channel, but also fed by another, parallel feed channel. The feed and the broth channels are separated by a dialysis membrane. The diffusive feed supply over the dialysis membrane enables constant feeding of substrate along the full length of the broth channel. Thus, Bioscope III makes it possible to generate controlled step changes in uptake rates, and the analysis of intracellular metabolite responses over time. This is unlike Bioscope I and Bioscope II where feed is introduced at a single point at the beginning of the broth channel and as a result, the feed concentration in the broth channel decreases along the broth channel due to consumption. There are two major differences between the Biocurve and the Bioscopes: First, the Biocurve is a well mixed device unlike the Bioscopes which are plug flow devices purposely designed to minimize liquid back mixing. The second major difference concerns sampling possibilities. As can be seen in figure 2 of chapter 2, the broth in the broth channel of the Biocurve can only be sampled at one point whereas in the Bioscopes, there are multiple sample points corresponding to different residence times along the broth channel.
Chapter 4 presents the characterization of Bioscope III in terms of mass transfer of oxygen and carbon dioxide over the silicone membrane and of glucose over the dialysis membrane. The experimentally determined mass transfer parameters were used to design biological experiments with Bioscope III using an aerobic glucose limited culture of S. cerevisiae supplied from a 4.0L conventional chemostat (D = 0.05h-1). The detailed analyses of those experiments are presented in chapter 4 and show that the characterization of Bioscope III was accurate.
In chapter 3 it was found that a modest 50% step increase in glucose supply to an aerobic glucose limited chemostat culture of S. cerevisiae caused an unexpected rapid and large mobilization of storage carbohydrates (glycogen and trehalose). This
18 surprisingly strong response to a very modest step change in glucose supply rate was followed up in chapter 5 that reports the response to a 100% step increase in glucose supply, where 13C-labeled substrate is employed. This experiment aims to study and quantify the metabolic response of especially the storage carbohydrate pools in more detail.
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Chapter 2
Characterization of an experimental miniature
bioreactor for cellular perturbation studies
This chapter has been published as:
Aboka FO, Yang H, de Jonge LP, Kerste R, van Winden WA, van Gulik WM, Hoogendijk R, Oudshoorn A, Heijnen JJ. 2006. Characterization of an experimental miniature bioreactor for cellular perturbation studies. Biotechnology and Bioengineering. 95(6), 1032 - 1042
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Abstract
A mini bioreactor (3.0mL volume) has been developed and shown to be a versatile tool for rapidly screening and quantifying the response of organisms on environmental perturbations. The mini bioreactor is essentially a plug flow device transformed into a well mixed reactor by a recycle flow of the broth. The gas and liquid phases are separated by a silicone membrane. Dynamic mass transfer experiments were performed to determine the mass transfer capacities for oxygen and carbon dioxide. The mass transfer coefficients for oxygen and carbon dioxide were found to be 1.55±0.17×10-5 m/s and 4.52±0.60×10-6 m/s respectively. Cultivation experiments with the 3.0 mL bioreactor show that (i) it can maintain biomass in the same physiological state as the 4.0 L fermenter (ii) reproducible perturbation experiments such as changing substrate uptake rate can be readily performed and the physiological response monitored quantitatively in terms of the O2 and CO2 uptake and production rates.
Introduction
The field of metabolic engineering emerged in recent years in response to the need to optimally exploit biochemical reactions which for centuries have provided mankind with pharmaceutical compounds, wine and beer and fermented food products such as cheese, bread, yogurt, and soy (Bailey, 1980). The concepts and methods of metabolic engineering have been applied to improve product yields of industrial strains, to broaden the range of substrates that cells can use and to diversify cellular product ranges (Stephanopoulos and Alper, 2004). Another example of successful application of metabolic engineering is the improvement of the ability of cells to flourish in alien environments that are toxic (Lee et al., 1994) or to withstand hypoxic fermentation conditions (Khosla and Bailey, 1988). Beyond the traditional industrial bioprocess applications, metabolic engineering is increasingly used in the field of medicine for the analysis of metabolism of whole organs and tissues as well as the identification of targets for disease control by gene therapy or nutritional strategies.
Metabolic engineering has been successful in a number of applications. Nevertheless many attempts to modify cellular metabolism have not yielded the desired results. Very often, over expression or knockout of enzymes lead to unexpected results (Nevoigt, 2008). This should not be surprising because
21 biochemical systems are very complex with many interactions between genes, metabolites and enzymes. The targets for gene manipulation should be carefully selected to improve the chances of a successful outcome. What is needed is a kinetic model of cellular metabolism which can allow identification of potential targets for genetic manipulation and prediction of the effects of such manipulation on metabolic systems (Hatzimanikatis et al. 1998). However, the development and application of kinetic models of biological systems is generally limited by lack of adequate knowledge of in vivo kinetic parameters and reaction mechanisms (Visser et al., 2004a). Progress in this area needs extensive, high quality experimental data (obtained with whole cells), from which to capture the underlying control mechanisms of the complex metabolic networks and the relevant in vivo kinetic parameters.
The responses of living cells to changes in their environmental conditions (Young et al., 2004) provide an excellent opportunity to generate information rich experimental data. There are many examples in literature of research efforts in which the cellular environment is perturbed and the subsequent response analyzed in order to gain a deeper insight of the control mechanisms of metabolic networks at the molecular level (Theobald et al., 1997; Visser et. al., 2004b). Environmental perturbation experiments are usually performed on (pseudo) steady state fermenter cultivations (the steady state being used as a reference condition). Setting up and running lab scale cultivation until a steady state is reached is generally a costly and time consuming affair. Once disturbed, the cultivation system may take a very long time to relax to the former steady state (if possible) or to reach a new steady state (Nguyen and Shien, 1995). This means that a steady state culture may allow for only one perturbation experiment. It is therefore necessary to improve perturbation strategies. To allow multiple perturbations with the same steady state culture, we propose a miniature bioreactor concept – The Biocurve (mini Bioreactor for Controlled Uptake Rate Variation Experiments) which allows multiple perturbation experiments with the same steady state culture and online measurement of the biological response to the perturbation.
The basic idea is that the organism of interest is cultivated in a lab or industrial scale bioreactor. The bioreactor supplies broth in a well defined (pseudo) steady state to the Biocurve which consists of a separate gas channel and a liquid channel separated by a thin (608 µm) silicone rubber membrane (figure 1).
22 Figure 1: (A) 3-D representation of the gas and liquid channels and the silicon membrane separating
the gas liquid channels of the Biocurve. (B) Top view of the liquid channel and the recycle loop configured between perspex blocks
The channels are milled into two identical perspex blocks which are assembled with the membrane in between. The broth for the Biocurve cultivation is drawn from the steady state bioreactor such that the steady state of the bioreactor is not disturbed, allowing many perturbation experiments with the same culture. In the Biocurve, the biomass can be conveniently exposed to different perturbation agents at different concentrations. Such experiments generate information on in-vivo kinetics or can be used to screen the effect of e.g. novel pharmaceutical compounds or nutrients on microorganisms. The Biocurve is equipped with sensors for O2 and CO2 allowing the biomass response to be easily monitored in terms of the oxygen consumption rate (qO2) and carbon dioxide production rate (qCO2), two very important physiological response parameters.
The first prototype of the Biocurve, presented in this research, has been characterized with respect to the quality of mixing and the mass transfer capacities for oxygen and carbon dioxide. Cultivation experiments have also been performed to test whether the Biocurve system can maintain biomass in the same physiological state as the bioreactor which supplied the biomass. In this paper, we present the characterization and validation studies, the latter being discussed mainly in terms of the biomass specific O2 consumption and CO2 production rates (qO2 and qCO2) which serve as the physiological state indicators of the biomass in the bioreactor and the Biocurve.
23
Theory and Methods
Reactor concept
The Biocurve concept is in many ways similar to the Bioscope developed by Visser et al. (2002). The main difference between the two is that the Bioscope is a plug flow device that can only be used for non steady state (pulse) perturbation experiments. The Biocurve on the other hand is a well mixed mini reactor in which the uptake rate of a perturbation agent (e.g. glucose) can be easily manipulated and also the responses of O2 uptake and CO2 production rates are measured online. The essential requirements for the Biocurve are: (i) a well mixed liquid phase which can support uniform growth of micro organisms due to absence of concentration gradients (ii) adequate and constant mass transfer capacities for oxygen and carbon dioxide to ensure sufficient and accurately known oxygen supply and carbon dioxide removal rates during perturbation experiments (iii) it should facilitate accurate online calculation of qO2 and qCO2 (iv) a small liquid volume to reduce the cost of using expensive perturbation agents and to allow many perturbation experiments with biomass from the same bioreactor. All of the above mentioned requirements have been experimentally validated.
Mixing of the broth
The Biocurve consists of a long broth channel (4.0 meters) with a high recycle flow which leads to high linear liquid velocities. The high recycle flow also serves to achieve the desired ideal mixing. The limit on the liquid flow rate that can be used arises from the pressure drop which is related to the liquid flow rate and the channel diameter by the following equation.
2 L x,L 4 L 1 64 ∆p = 4f ρv where 4f = ∆p L (1) d 2 Re d Φ ⋅ ⋅ ⇒ ∼
The pressure drop across the liquid channel will therefore strongly increase with decreasing channel diameter (d) and increasing channel length (L) and liquid flow (ΦL). The maximum allowable pressure drop in the liquid channel was set at 2.0 bars which is close to the hydrostatic pressure at the bottom of most industrial bioreactors. The 4.0 meter long channels have a hemi-circular cross-section with a diameter of 1.2 mm. The quality of mixing in the liquid phase of the Biocurve was evaluated in terms of a dimensionless Péclet (Pe) number (Perry and Green, 1997).
24 x,L ax v L Pe = (2) D
where vx,L [m/s], is the liquid linear velocity which was varied between (0.1 and 0.6 m/s) and L [m] is the characteristic length of the reactor and Dax is the axial dispersion. For systems with large axial dispersion (i.e. well mixed flow), Pe is close to zero while for low dispersion systems, Pe tends to infinity. The Pe is calculated from the mean residence time ( t ) and the variance (
σ
t) around the mean according to equation 3 (Levenspiel, 1999; Visser et al., 2002).2 2 t 2 σ 1 1 = 2 +8 (3) t Pe Pe
The two parameters can be obtained from the normalized cumulative tracer response, also known as the F (t) - curve (equations 4 and 5).
0 t = t E(t) dt (4) ∞ ⋅ ⋅
∫
2 2 t 0 σ = (t- t) E(t) dt (5) ∞ ⋅ ⋅∫
Estimation of the mass transfer coefficients
To enhance O2 and CO2 transfer per m3 of broth, the channel depth was chosen to be small (1.2 mm). The overall mass transfer coefficient for oxygen (i = O2) and carbon dioxide (i = CO2) can be obtained from their respective liquid or gas phase mass balances, which in the absence of consumption and production terms, can be written as
i,L
L L,in i,Lin L i,L i i,g i,L
i
dC P
V = C - C +k A( x - C ) (6)
dt Φ Φ m RT
i,g
g g,in i,g,in g i,g i i,g i,L
i
dx
P P
V = x - x - k A( x - C ) (7)
RT dt
φ
φ
m RTThe outgoing gas flow rate
φg is obtained from the steady state nitrogen balance in the
gas phase25 O2,g,in CO2,g,in g g,in O2,g CO2,g 1-x -x = (8) 1-x -x
φ
φ
The above equations assume that Henry’s law applies and that there is a uniform gas phase concentration. Henry’s constants for oxygen (mO2 = 34.37) and carbon dioxide (mCO2 = 1.39) at 300C were taken from Bloemen et al. (2003). Experiments conducted typically involved step changes in the gas phase composition and monitoring of the resulting liquid and gas concentrations in dynamic and steady state.
Estimation of q
O2and q
CO2In the Biocurve cultivation experiments where the biomass is subjected to certain perturbations, the overall mass transfer coefficients for O2 (kO2) and CO2 (kCO2) and the measured concentrations of oxygen in the gas and broth and measured liquid flow rates allowed estimation of qO2 and qCO2 through the liquid phase mass balance for O2 and CO2 (equation 9), which now include the O2 consumption or CO2 production terms.
i,L
L L,in i,L,in L i,L i i,g i,L i x L
i
dC P
V = C - C + k A( x - C ) + q C V (9)
dt Φ Φ m RT
26
Experimental
Technical set up of the Biocurve
A scheme of the Biocurve assembly is shown in figure 2. The perspex block and the recycle loop are placed in a temperature controlled box. A thermocouple monitors air temperature inside the box, which is maintained at 30.00C, (same temperature as the operating temperature of the bioreactor, which is the source of the steady state biomass) by two 100 Watt light bulbs that light up when the air temperature drops by 0.10C below the set point. The typical gas and liquid (broth) flow rates entering the gas and liquid channels are in the range 0.60-1.80 nL-gas/h and 30.0-150.0 mL-broth/h respectively while the recycle flow rate in the liquid/broth channel was 1200.0 mL/h. Three peristaltic pumps (A, B and C) are used in the Biocurve set up as can be seen in figure 2. Pump A is used for the external recycle of the bioreactor. The broth feed to the Biocurve is drawn from the bioreactor recycle loop in such a way that no bubbles enter the Biocurve, hence the name “de-bubbler” for the bioreactor recycle loop. Pump B is used to feed the bubble free broth to the Biocurve. Pump C is the Biocurve recycle pump.
The exhaust gas from the Biocurve is analyzed online with a Fisher-Rosemount Gas analyzer (NGA 2000, Fisher-Fisher-Rosemount, Hasselroth, Germany) after passing through a Perma Pure membrane dryer (MD 110-72-48F, Perma Pure, Toms River, New Jersey). On line sensors for dissolved CO2 (model: CO2 5100e, Mettler Toledo, Switzerland) and pH (Applikon, The Netherlands) are installed in the recycle loop. Two miniaturized Clark type dissolved O2 sensors (Applikon, The Netherlands), are used in the set up, one at the inlet to the liquid channel and the other is placed in the recycle loop. The pH is controlled at 5.0 by automatic addition of 3.0 M KOH solution. The base and the feed solutions are supplied by syringe pumps. For the experiments where O2 and CO2 transfer rates are manipulated, a system of two solenoid valves and mass flow controllers (MFC) helps with the instantaneous exchange of one feeding gas for another while keeping the mass flow rate of the entering gas constant. The gases used for the Biocurve cultivation experiments were obtained by mixing O2 and N2.
RTD experiments
Residence time distribution (RTD) of the Biocurve as a function of the recycle flow rate, was determined by measuring the response to a step change in a tracer (10.0 mM
27 KCl(aq)) concentration. The evolution of the tracer concentration in the liquid exiting the Biocurve liquid channel was monitored with an electrical conductivity detector (DIONEX ED 40). The step change experiments were performed with the help of two solenoid valves placed at the inlet of the liquid channel. The response time of the valves was less than one second after which the tracer solution started to flow into the tubing in front of the valves.
Tracers for RTD experiments can be any non-reactive substance that is easily detectable when used in small quantities. Dyes, for example are good tracer materials provided suitable optical detection systems are available such as the one developed by Vallejos et al., (2005a). They have successfully applied their novel optical system to study mixing in a 12.5 mL mini bioreactor equipped with a paddle impeller for different rotational rates ranging from 10 rpm to 1000 rpm (Vallejos et al., 2005b).
Mass transfer experiments
Mass transfer experiments for oxygen were performed by applying step changes in the gas composition entering the gas channel while continuously passing a liquid through the liquid channel. The step changes (pure N2 to air or vice versa) were done after allowing the liquid phase to reach equilibrium with pure N2 or air. Mass flow controllers were used to ensure a constant volumetric flow rate of the gases before, during and after the interchange of the entering gases. The liquids (de-mineralized water and non-respiring yeast broth) were used in these experiments to study the effect on the overall mass transfer coefficient, ki. The non respiring yeast broth contained approximately 14.5 gDW/L yeast biomass. This is the same steady state biomass concentration as in the bioreactor which supplies the inoculum biomass to the Biocurve. Carbon dioxide mass transfer experiments were similar to those for oxygen except that instead of air, special gases containing specified amounts of carbon dioxide (3.00% or 1.00% CO2) were used. Continuous readings of the concentrations of O2 and CO2 in the liquid and off gas were taken during these experiments and equations 6, 7 and 8 were used to estimate the overall mass transfer coefficients (kO2 and kCO2) from these measurements. The permeability of O2 and of CO2 in the silicon membrane was quantified separately using a gas-membrane-gas permeation set up (courtesy of DSM Research, The Netherlands).
Fermenter cultivations
Aerobic glucose limited cultivation, at D = 0.05 h-1, of the yeast Saccharomyces cerevisiae was carried out in a 7.0 L lab scale continuous stirred tank bioreactor
28 (Applikon, The Netherlands) with a working volume of 4.0 liters. The medium was based on that of Verduyn et al. (1992) and contained 27.10 g/L of glucose and 1.42 g/L of ethanol, allowing a biomass concentration of 14.5 g/L. Ethanol was present to suppress oscillations. The stirrer speed was 600 rpm. The broth temperature was maintained at 30 0C and the pH was monitored using a pH probe (Applikon, The Netherlands) and kept at 5.0 by automatic addition of 4.0 M KOH solution. Air supply to the bioreactor was controlled at 200.0 nL/h by a mass flow controller. Dissolved oxygen concentration was measured using an oxygen sensor (Applikon, The Netherlands). The bioreactor was pressurized at 1.3 bars by controlling the exit gas opening with a needle valve. The steady state biomass growth under these conditions was expected to be fully respirative, with a respiratory quotient (RQ) close to one. Steady state was assumed when at least five residence times had passed since inoculating the bioreactor and the biomass concentration and O2 and CO2 concentrations in the gas phase and in the broth were constant.
Biocurve cultivation experiments
All Biocurve cultivation experiments were preceded by calibration of the Biocurve sensors for pH, and O2 and CO2 in the gas and liquid. A buffer solution with a salt content similar to the feed medium, except for the absence of glucose, trace minerals and vitamins - was used for calibration. The 0.0% measurement of the dissolved oxygen sensor was calibrated using pure nitrogen gas in the gas channel and nitrogen sparged buffer solution in the liquid channel while the 100.0% (air saturation) was calibrated using air in the gas channel and air sparged buffer solution in the liquid channel. The gas analyzer was calibrated simultaneously with the dissolved oxygen and carbon dioxide sensors. This was possible because the gas flow rate at the start and during the calibration was the same as the flow rate used in the cultivation phase. Continuous readings of O2 and CO2 concentrations in the gas and liquid phases were recorded during Biocurve cultivation experiments.
The Biocurve can be operated in two modes which will hereafter be referred to as mode I (continuous biomass feed) and mode II (no biomass feed). In mode I, the Biocurve is continuously supplied with biomass at 30.0 - 150.0 mL/h from the recycle loop on the fermenter. This broth flow to the Biocurve is part of the total bioreactor effluent of 200.0 mL/h. To maintain the bioreactor in steady state, the remaining effluent is separately removed as shown in figure 2.
In mode II, the biomass supply is cut off after the liquid volume is filled up with broth. Feed supply and broth withdrawal (at the same rate feed supply) remains
29 after cutting off biomass supply. Hence, mode II is in essence a chemostat operation at D = 0.05h-1.
Results and Discussion
The main objective of this study was to develop an experimental mini bioreactor system to allow multiple perturbations with the same bioreactor culture. The result is the Biocurve set up already described in the experimental section. The Biocurve should maintain physiology of biomass as well as environmental conditions and it should allow accurate online measurement of perturbation response in terms of qO2 and qCO2. The experimental validation of these requirements is presented and discussed in this section. The Biocurve was first characterized with respect to liquid phase mixing and mass transfer capacities for oxygen and carbon dioxide. Next, cultivation experiments were performed to test whether the Biocurve can maintain the physiological state of biomass (in terms of qO2 and qCO2) identical to the steady state values in the bioreactor which supplied the broth to the Biocurve.
Figure 3: (a) Normalized tracer response curve when R = 0 and ΦL = 2.0 mL/min and (b) the
30 Figure 4: (a) Normalized tracer response curve when R =10 and ΦL = 2.0 mL/min and (b) the
corresponding E(t) curve
Liquid Phase Mixing
RTD experiments were performed with different recycle ratios (R) defined as the ratio of the recycle flow rate to liquid through flow rate. Typical normalized experimental tracer response curves (F(t)) and the corresponding differential tracer response curves (E(t)) are presented in figure 3 (R =0) and figure 4 (R =10.0). The Pe numbers calculated using equation 3 are summarized in table 1. The Pe number for experiments without a recycle flow (R = 0) was 44.9±2.2 (8 independent experiments) while for R = 10.5, the Pe number was 4.4±0.1 (5 independent experiments). These results show that the liquid phase in the Biocurve is well mixed confirming a well established fact that a high recycle flow will enhance mixing (Buffham and Nauman, 1975).
Table I. Summary of the Pe numbers obtained with different recycle ratios
Through flow (mL/min) Recycle Flow (mL/min) Linear Velocity in the broth channel (m/s) Recycle Ratio (-) Pe (-) 1.94 0 0.06 0 44.9±2.2 2.90 0 0.09 0 36.7±2.4 1.94 6.7 0.20 3.5 5.2±0.2 0.56 3.1 0.09 5.5 4.5±0.0 2.00 12.0 0.35 6.0 4.6±0.3 3.01 18.0 0.53 6.0 4.5±0.1 1.94 20.4 0.60 10.5 4.4±0.1
31
Table II. Resistance to O2 and CO2 transport in the membrane and the liquid boundary layer
and the overall value
1/ki (overall)
1/(miki,M) (membrane)
1/ki,L
(liquid boundary layer)
(m/s)-1 (m/s)-1 (m/s)-1
Oxygen 6.45×104 4.14×104 2.31×104
Carbon dioxide 2.21×105 1.97×105 2.42×104
Mass transfer
Figure 5 shows typical dissolved oxygen (dO2) profiles obtained in step up/ step down mass transfer experiments with a liquid through-flow of 84.0 mL/h, a recycle flow of 1200.0 mL/h and a gas flow of 1.80 nL/h. The gas entering the gas channel was interchanged between air and pure nitrogen. The liquid was always saturated with air (20.95 %O2). Experimental curves obtained at different liquid through flow but constant recycle flow rate and gas flow rates presented similar behavior allowing accurate kO2 estimates. The dissolved CO2 (dCO2) profile is presented in figure 6. This profile was obtained at a liquid through flow of 90.0 mL/h, a recycle flow of 1200.0 mL/h and a gas flow of 30.0 mL/h. The gas entering the gas channel was changed from 3.0% CO2 gas to 1.0% CO2 gas and then back to 3.0 % CO2 gas while CO2 free liquid was entering the liquid channel.
Figure 5: Experimental (two duplicate experiments, dashed lines) and simulated (continuous line)
profiles of dissolved oxygen obtained during step-up/step-down mass transfer experiments with a liquid through flow rate was 84.0 mL/h, a recycle flow rate of 1200.0 mL/h and a gas flow rate of 1800.0 mL/h
32 Figure 6: Experimental (dashed line) and simulated (continuous line) profiles of dissolved carbon
dioxide obtained during step-up/step-down mass transfer experiments with a liquid through flow rate of was 90.0mL/h, a recycle flow rate of 1200.0 mL/h and a gas flow rate of 1800.0 mL/h
Equations 6, 7 and 8 were used to estimate the kO2 and kCO2 values from the measured steady state O2 and CO2 concentrations in the gas and liquid and the measured gas and liquid flow rates. The estimated kO2 value is 1.55±0.17×10-5 m/s (from 25 independent experiments) and kCO2 is 4.52±0.60×10-6 m/s (from 7 independent experiments). There was no significant difference between the values obtained with water and non respiring broth. These values were used in a mass transfer model of the Biocurve to simulate the dynamic experiments. The simulated dissolved O2 and CO2 profiles are included with the experimental profiles in figures 5 and 6 respectively. Simulations were performed with MATLAB (The Mathworks, Inc., and Natick, MA). It can be seen that the theoretical profiles accurately describe the experimental data points.
The small discrepancy in the evolution of the theoretical and experimental dissolved oxygen profiles is probably due to the fact that sorption/de-sorption dynamics of oxygen in the silicon membrane was not accounted for in the mass transfer model. Instead, a simplifying assumption was used in the model that the steady state O2 and CO2 concentration in the silicone membrane is instantaneously reached. However, the sorption/de-sorption dynamics of O2 and CO2 in the silicone membrane are absent from the steady state portions of the experimental data used to estimate the kO2 and kCO2 values. The steady method also avoids the complexities that may arise from simultaneous interfacial transport of different species through the membrane (Linek et al., 1987).
33 Figure 7: Linear regression fit to equation 7 of CO2 data from 16 independent Biocurve cultivation
experiments
The mass transfer coefficients for oxygen and carbon dioxide in the silicone membrane (kM,O2 = 7.03×10-7 [m/s] and kM,CO2 = 2.97×10-6 [m/s], respectively) were separately determined in a gas-membrane-gas permeation test set up. This allowed estimation of the contribution of the membrane and liquid boundary layer resistances to the overall resistance to O2 and CO2 transport in the gas-membrane-liquid layers (table 2). The relationship of the mass transfer resistances in the different layers is outlined in appendix.
According to table 2, approximately 65 % of the total resistance to O2 transport lies in the membrane while 89% of the total resistance to CO2 lies in the membrane. The liquid boundary layer resistance to CO2 (1/kCO2,L = 2.42×104m/s)-1 in table 2) was estimated from 1/kO2,L, using their diffusion coefficients (Bloemen et al., 2003). The calculated membrane resistance to CO2 transport in table 2 is about 19.0 % lower as measured in the gas-membrane-gas set up. Such a small difference is acceptable considering the different methods used.
The O2 and CO2 measurements during Biocurve cultivation experiments can also be used to estimate the mass transfer parameters via the gas phase balances (equation 7 and 8). In that case, there is O2 consumption and CO2 production, in contrast to the mass transfer experiments using water and non respiring broth. Steady state data on CO2 production from 16 independent cultivation experiments were combined in a linear regression fit using equation 7. Figure 7 shows the results of the fit with R2 equal to 0.97 and kCO2 equal to 5.02×10-6 m/s which is close to 4.52×10-6 m/s obtained from the CO2 mass transfer experiments, presented earlier. The variations applied in the 16 Biocurve experiments were different ingoing gas flow
34 rate; different substrate feed rates, and different biomass supply rates. Because of the limited accuracy of the O2 analysis in the gas phase, the corresponding O2 measurements in the 16 experiments were too scattered for linear regression analysis.
Lab scale cultivations
The cultivation conditions in the fermenter at µ = 0.050 h-1 (see experimental section) yielded a fully respirative steady state culture of Sacharomyces cerevisiae with a cell density of 14.5 gDW/L. The steady state qO2 and qCO2 were 0.039±0.002 molO2 /C-mol biomass/h and 0.037±0.002 /C-molCO2/C-mol biomass/h respectively. In steady state, more than 95 % of the carbon and electrons fed to the fermenter was accounted for through their respective balances.
Biocurve cultivations
The biomass concentration (Cx) during the Biocurve cultivation experiments (modes I and II) remained relatively constant and close to the steady state value of 14.5 gDW/L in the fermenter. The change in biomass in the Biocurve can be described by the biomass balance
(
)
x L L x,in x x x L dC V = Φ C - C + q C V (10) dtThe steady state fractional increase in biomass concentration in mode I is obtained from equation 10 to be x x,in x C 1 = (11) C 1 q τ−
Thus, in mode I, the fractional increase in biomass is a function of the specific biomass growth rate (qx) and residence time (τ) only. The short residence time for mode I experiments (typically less than 10 minutes) combined with low qx (0.05 h-1), meant that the change in biomass concentration in the Biocurve could be neglected. In mode II, the Biocurve was operated as a chemostat with the same dilution rate, D = 0.05 h-1 as the dilution rate of the fermenter which supplied the broth. Furthermore, the feed (27.10 g/L of glucose and 1.42 g/L of ethanol) for the Biocurve experiments was identical to the feed for the bioreactor cultivations. Hence, in mode II
35 experiments, the biomass concentration in the Biocurve was expected to be the same as in the fermenter.
In order to maintain aerobic cultivation conditions in the Biocurve, it was necessary to supply oxygen enriched air to the gas channel unlike in the lab scale cultivation where normal air (20.95 %O2) was sufficient. In the fermenter, under the cultivation conditions described in the experimental section, kO2a = 0.084s-1 which is related to the sparged bubbles. These bubbles are of order of several mm in diameters for which kO2 ≈ 2.0×10-4 m/s (van’t Riet and Tramper, 1991) giving a ≈ 420 m2/m3. In the Biocurve, kO2 = 1.55×10-5 m/s and a = 1.6 ×103 m2/m3 giving kO2a = 0.025s-1. The lower kO2 in the Biocurve is due to the resistance to O2 transfer in the 608 µm silicon membrane. It is also important to note that the CO2 transfer capacity in the Biocurve is approximately three times less than that for O2 unlike in the bioreactor where the transfer capacities for O2 and CO2 are approximately equal. It was therefore expected that under growth conditions leading to the same qO2 and qCO2 in the Biocurve as in the fermenter, the dissolved CO2 level will be higher in the Biocurve compared to the fermenter.
When fed with the same qs as in the fermenter, the biomass growing in the Biocurve was expected, in steady state, to be fully respirative with the same qO2 and qCO2 as in the fermenter. Figures 8 and 9 show respectively typical steady state dO2, dCO2 and the estimated qO2 and qCO2 profiles obtained with the Biocurve in mode I (figure 8) and mode II (figure 9). The same O2 enriched gases (36.79 and 25.80 % O2) at a flow rate of 801.0mL/h were used in the gas channel for both experiments. For mode I, biomass was continuously supplied to the Biocurve at 58.40 mL/h. The steady state qO2 and qCO2 in mode I (shown in figure 8) are nearly the same, 0.037 mol/C-mol biomass/h and close to the above mentioned value in the bioreactor. The steady state qO2 and qCO2 for mode II in figure 9 are also closely the same, 0.036 mol/C-mol biomass/h and close to the value in the bioreactor.
After the initial steady state had been reached in both experiments, the 36.79% O2 gas entering the gas channel was interchanged with 25.80% O2 gas (after approx. 85 minutes in figure 8 and 45 minutes in figure 9). Following the change in the gas composition, the dO2 decreased from the initial steady state value (figures 8 and 9) to a new steady state and in both cases, the former steady state was recovered upon switching the entering gas from 25.80% O2 gas back to 36.79% O2 gas (after approx. 135 minutes in figure 8 and 80.0 minutes in figure 9). The mass transfer model of the Biocurve, augmented with O2-consumption and CO2-production terms was used to simulate the dO2 and dCO2 profiles (included in the figures 8 & 9). It can be seen that the theoretical dissolved oxygen profiles closely match the experimental profiles in both experiments which shows that the model accurately describes the experimental
