Online Detection of Feed Demand in High Cell Density Cultures of e.coli by Measurement of Changes in...

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Online Detection of Feed Demand in HighCell Density Cultures of Escherichia coli byMeasurement of Changes in DissolvedOxygen Transients in Complex Media

Victoria S. Whiffin,1 Michael J. Cooney,2 Ralf Cord-Ruwisch1

1School of Biological Sciences and Biotechnology, Murdoch University, SouthStreet, Murdoch 6150, Western Australia; telephone: +61 8 9360 2403;fax: +61 8 9310 7084; e-mail: [email protected] Natural Energy Institute, University of Hawaii at Manoa,Honolulu, Hawaii

Received 13 January 2003; accepted 9 July 2003

Published online 2 January 2003 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/bit.10802

Abstract: A starvation-based dissolved oxygen (DO)transient controller was developed to supply growth-limiting substrate to high cell density fed-batch culturesof recombinant Escherichia coli. The algorithm adjusted apreexisting feed rate in proportion to the cultures oxygendemand, which was estimated from transients in the DOconcentration after short periods of feed interruption. Inthis manner, the addition of glucose feed was preciselycontrolled at a rate that did not exceed the acetateproduction threshold, thus preventing acetate accumula-tion. In comparison to exponential feed algorithmscommonly used in industry, the implementation of thenew feeding strategy increased the final cell density from32 to 44 g (dry cell weight).L1, with less than 16 mMacetate accumulated, producing an ideal culture for

subsequent induction. Despite a constant starvation leveland relatively low levels of acetate, experimental culti-vations still tended to produce acetate towards the end ofthe process. The use of a simple Monod model providedan explanation as to why this may occur in high celldensity cultivations and suggests how it may be over-come. B 2004 Wiley Periodicals Inc.

Keywords: Escherichia coli; high cell density culture;acetate; dissolved oxygen transients; fed batch; feedingstrategy

INTRODUCTION

High cell density cultivation techniques have been

developed to achieve high final biomass and productyields in industrial cultivation (Lee, 1996). In order to

achieve high cell density, the stresses that usually limit

cell growth and recombinant protein expression must be

removed, thus allowing continued growth and maximumexpression of recombinant products (Lee, 1996; Riesen-

berg and Guthke, 1999). For cultivation of Escherichia

coli, the most significant of these stresses is the production

of acetate, which accumulates when the acetyl-CoA

degrading pathways (e.g., TCA cycle) have reached their

capacity, forming a metabolic bottleneck (Akesson et al.,

1999; Bech Jensen and Carlsen, 1990; Lee, 1996; Luli and

Strohl, 1990; van de Walle and Shiloach, 1998). Previous

approaches to limit the accumulation of acetate have

included genetic engineering to provide the organisms with

acetate degrading enzymes (Bermejo et al., 1998), acetate

detection using sophisticated analytical equipment such as

high performance liquid chromatography (HPLC) and

acetate removal by dialysis membrane technologies (Land-

wall and Holme, 1977; Saucedo and Karim, 1996;

Schugerl et al., 1996; Turner et al., 1994). These

approaches have had limited use in industrial-scale

fermentations because of their complexity and limited

usability under sterile conditions.

An ideal industrial controller is simple, robust, and as

nonintrusive as possible. It should be sensitive enough to

accurately provide the required amount of feed in the

presence of nonlinear growth and complex media such as

yeast extract and tryptone. A commonly used industrial

approach is to control the cultures growth rate by themetered addition of a growth-limiting substrate (i.e., fed-

batch cultivation) (Lee, 1996). A number of substrate

delivery strategies have been proposed, which can be

broadly grouped into two categories; those that operate

independently of the reactor predictive control, and those

that operate in communication with the reactorfeedback

control (Hangos and Cameron, 2001). Although predictive

controllers are useful for fixed and well-established

processes, they are unable to respond to process

B 2004 Wiley Periodicals, Inc.

Correspondence to: Ralf Cord-Ruwisch

Contract grant sponsors: CSL Limited (Melbourne, Australia)

Murdoch University (Perth, Australia)


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disturbances, new media compositions, or strain substitu-

tions and hence have limited application in a changing

industrial environment.

Feedback control strategies are designed to detect real,

online changes in the system that indicate overfeeding or

underfeeding and couple these changes to an adjustment in

feed rate. The primary parameters used for feedback

control in E. coli fed-batch cultivations are pH (Lee, 1996;

Lee and Chang, 1993, Robbins and Taylor, 1989; Suzuki

et al., 1990), dissolved oxygen (DO) (Bauer and Shiloach,

1974; Cutayar and Poillon, 1989; Kleman et al., 1991;

Konstantinov et al., 1990; Konstantinov and Yoshida,

1990; Shiloach et al., 1996), glucose (Kleman et al., 1991;

Luli and Strohl, 1990), or acetate data (Turner et al., 1994).

Because of its sensitivity, versatility, and online avail-

ability, oxygen-based feedback controllers are widely used.

Oxygen-based feedback strategies have been developed

from the observation that the depletion of substrate causes

a sharp increase in DO (Bauer and Shiloach, 1974; Cutayar

and Poillon, 1989; Kleman et al., 1991; Konstantinov and

Yoshida, 1990; Shiloach et al., 1996). More recently,

Akesson et al. (2001) proposed a pulse feeding strategy

that applied periodic up- and down-pulses to the feed rateto recombinant cultures of E. coli grown in defined

media. By evaluating the DO response to the pulses, the

presence or absence of glucose in the medium was in-

ferred. For example, an increase in DO in response to a

25% decrease (down-pulse) in the feed rate signified

glucose limitation. Concomitantly, a decrease in DO in

response to a 25% increase (up-pulse) in feed rate signified

glucose starvation (Akesson et al., 1999). More recently,

Johnston et al. (2002, 2003) considered the application of

periodic up pulses (i.e., feed-up DO transient control) to

medium and high cell density cultures of recombinant

E. coli grown in complex media. These results showed that

complex media provides a difficult environment for

aggressive acetate threshold tracking and suggested that

controllers attempting to track the acetate threshold in

complex media should avoid using algorithms that ag-

gressively probe (i.e., exceed) the threshold as a means to

improve performance.

By contrast, the approach taken in this study tracks the

acetate production threshold by providing sufficient

glucose feed to cells only when they are starved and

therefore are fully able to respire the added feed. This

feed on demand pulse strategy approach first probes

the cultures ability to respire substrate and then provides

extra substrate only when the cells have shown a demandfor it.

We have built upon the feed on demand approach

originally presented by Akesson et al., (2001) to develop a

simple, industrially useful feeding strategy that is capable

of producing high cell density cultures of E. coli in

complex media, while preventing the unwanted acetate

production that occurs with more aggressive probing

controllers and avoiding the consequent reduced efficiency

of recombinant production (Johnston et al., 2003).

MATERIALS AND METHODS

Operating Principle of the StarvationDO-Transient Controller

In order to specifically provide feed on demand, a

measurement is necessary that evaluates the bacterial

starvation level (i.e., substrate limitation or saturation).

This measurement can be obtained by briefly interrupting

the feed supply; a substrate-limited culture will immedi-

ately lower its oxygen uptake activity, resulting in anincrease in DO, while a substrate-saturated culture will

not respond. For sustained growth at high rates without

causing oversupply of substrate and hence acetate

accumulation, the bacterial cells should always be kept

under substrate limitation.

It is not sufficient to simply consider absolute changes

in DO to feed interruptions as an indicator of sub-

strate limitation in the reactor. For example, if the

microbial oxygen uptake rate (OUR) changes from 240

to 200 mg.L1.h1, the resulting change in steady-state

DO will be from 1 to 2 mg.L1 (DO change = 1 mg.L1)

or from 5 to 5.33 mg.L

1

(DO change = 0.33 mg.L

1

).This is because the microbial DO consumption rate is not

linearly correlated to changes in DO concentration, due to

the variable rate of oxygen transfer into the solution,

which is DO concentration-dependent. As long as the kLa

is constant, the bacterial OUR can be obtained from the

Figure 1. Boolean schematic of DO_transient strategy used to control

fermentations (FR = feed rate; DOT = dissolved oxygen transient). Three

adjustable parameters are available: 1) feed off duration, 2) starvation level

between DOT(1) and DOT(2), and 3) feed on duration.

WHIFFIN ET AL.: ONLINE DETECTION OF FEED DEMAND 423


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oxygen steady-state concentration. Because true oxygen

steady-state concentrations never establish in our system,

we introduce the term DO-transient as an approximation

of the OUR [Eq. (1)]:

DOT kLa cL cS 1

Where DOT is the DO-transient (mg.L1.h1); kLa is the

mass transfer coefficient (h1); (cS cL) is the oxygen

saturation deficit (mg.L1) where cS is the dissolved

oxygen concentration at saturation and cL is the current

dissolved oxygen concentration. The DOT is closely relatedbut not identical to OUR, as the steady-state dissolved

oxygen concentrations essential for proper OUR calcula-

tions are not reached in the 30-sec feed off intervals.

The DO-transient controller determines the appropriate

glucose-feeding rate by quantifying the relative change in

DO-transient immediately before (DOT(1)) and after

(DOT(2)), a period of feed interruption. If DOT(2) is

significantly lower than DOT(1) then the feed rate is

increased by a specific factor (Eq. 2). Conversely, if DOT (2)is not significantly lower than DOT(1), then the feed rate is

decreased by another factor (Fig. 1, Eq. 3):

If DOT2 < DOT1 S then FRINC 1:01 2

If DOT2 z DOT1 S then FRINC 0:99 3

FRNEW FROLD FRINC 4

Where DOT is dissolved oxygen transient; S is starvation

level; FR is feed rate; FRINC is feed rate increment.

The DO-transient controller has three adjustable parame-

ters: Feed off duration equals the interval of short feed

interruption during which flow rate of glucose to the culture

is equal to zero, Starvation level equals the percent

difference that is tolerated between DOT(1) and DOT(2) to

determine a yes or no decision by the controller, and Feed

on duration equals the time interval between feedinterruptions (Fig. 1). In all trials using the DO-transient

controller, the feed off duration was 0.5 min, the feed rate

increment was 1%, and the feed on duration was 2 min. The

starvation level was variable between trials.

Microorganism

The microorganism used was recombinant E. coli BL21

DE3 (CSL, Victoria, Australia). To ensure genetic stability,

a working seed bank was established and stored at 80jC

in 15% (v/v) glycerol stabilised cryogenic vials.

Inoculum and Media

Seed inocula were prepared by inoculating a vial taken

from the working seed bank into 100 ml of SL broth

(Table I ) and grown overnight at 37jC on a 200 rpm orbital

flat bed shaker for 18 h. The entire 100-ml culture was then

aseptically transferred to the bioreactor. The medium for

fed-batch cultivation was complex (Table I). Kanamycin

was added to all media to maintain selective pressure for

the recombinant plasmid.

Fed-Batch Cultivations

Bioreactor and Processing Parameters

Fed-batch cultivations were conducted in a 1-L bioreactor

(B. Braun Biolab, Melsungen, Germany, mini-fermenter).

Reactor conditions were: liquid volume 700 750 ml, initial

stirring speed 150 rpm, which was varied in discrete steps

(150, 200, 300, 400, and 500 rpm) whenever the dissolved

oxygen went below 2 mg.L1, 37jC, oxygen supplied as

100% O2, automated pH control with 5M NH4OH and lower

set-point of 6.8, and antifoam control by Antifoam C

Emulsion (Sigma, St. Louis, MO, Cat. A 8011). Feeding was

commenced and regulated by the DO-transient controller

after a 2-h batch phase. The DO-transient controller was

designed in-house and coded in LabViewy (National

Instruments, Baltimore, MD, v. 3.1.1) to automate

the fed-batch addition of substrate. Data logging of

dissolved oxygen and pH data from the reactor was

sent to the DO-transient controller, which used theinformation to perform an appropriate control deci-

sion, and returned an altered voltage signal to a vari-

able speed drive substrate pump (Watson Marlow

313U). Precise automated addition of substrate was

aided by online detection of weight changes of the sub-

strate vessel.

Table II. Comparison of cultivation outcome baseline data trial and

DO-transient control trials.

Trial Baseline 2% DOT 5% DOT 2 7 % DOT

Biomass (g (DCW).L1) 33 45 51 44

Accumulated acetate (mM) 48 620 63 16

Glucose added (mM) 727 800 753 600

Residual glucose (mM) 1 1.8 2 0.5

SGUR (mmol.gX.h1) 5 4 4 3 3 1.3 1.8 1.6

SAPR (mmol.gX.h1) 0.5 1.5 2 5 0.3 f0

Biomass yield (gX.gS1) 0.25 0.31 0.39 0.40

Fermentation time (h) 12 12 15.4 17

Biomass productivity (gX.h1) 2.75 3.75 3.31 2.59

Starvation level 2% 5% 2 7%

Table I. Media and feed composition.

SL broth Batch media (g.L1) Feed solutio n ( g.L1)

Glucose 500.0

Tryptone 26.8 160.8

Yeast extract 21.4 128.4

NaCl 8.5

MgSO4.7H2O 0.9

K2HPO4 5.4

NaH2PO2.2H2O 1.6

Kanamycin 0.05

424 BIOTECHNOLOGY AND BIOENGINEERING, VOL. 85, NO. 4, FEBRUARY 20, 2004


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Samples (5 ml) were periodically removed from the

reactor through a sample port, immediately centrifugedat 13,500 rpm, and the supernatant frozen for subse-

quent analysis.

Biomass dry weights were determined by correlating

optical density (600 nm) measurements against a calibra-

tion curve. Calibration curves were generated by creating

serial dilutions (taken from biomass samples at the end of a

cultivation) in which the optical density was measured and

known volumes were transferred into preweighed test

tubes, centrifuged, the clear supernatant discarded, and the

tubes left to dry overnight (12 h) at 80jC. After cooling in a

desiccator, the tubes were reweighed and the differenceused to calculate dry cell weights.

Analytical Methods

Residual Glucose

Residual glucose in the culture broth was measured by

enzymatic analysis [Yellow Springs Instruments, Youngs-

Figure 3. Cultivation profile for fast linearly fed trial using conventional exponential feeding algorithm until hour 6 and then switched to maintain a

linear feed rate of 44 mM glucose.h1 for the rest of the cultivation. Total glucose added (mM) (.), residual glucose in the reactor (mM) (o), acetate (mM)

(5), biomass (g (DCW).L1) (E).

Figure 2. Cultivation profile for baseline data trial using conventional exponential feeding algorithm. Total glucose added (mM) (.), residual glucose in

the reactor (mM) (o), acetate (mM) (5), biomass (g (DCW).L1) (E).



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town, OH (YSIy) 2700 Select]. Frozen samples were

completely thawed and vortexed to ensure sample homo-

geneity. Standards were injected every 10 measurements to

ensure the baseline had not shifted.

Acetate

Acetate was measured by gas chromatography (Varian, San

Fernando, CA, Star 3400 Gas Chromatograph). The GC was

set up with an Econo-cap EC1000 (FFAP) 15 m 0.53 mmcolumn (Alltech, Deerfield, IL), a flame ionisation detector

(FID), a splitless injector set at 230jC, high purity nitrogenas a carrier gas, and the following column temperature

program: initial column temperature 80jC, ramped to 140jC

at a rate of 40jC per minute, 140jC for 1 min, ramped to

230jC at a rate of 50jC per minute, and held at 230jC for

2 min for column flushing. The total program was complete

in 6.5 min. Standards were injected every 10 measurements

to ensure the absence of baseline shift.

Where necessary, samples were diluted with deionised

water to concentrations between1 20 mM (0.06 1.2 gl1).All samples were acidified with 10% formic acid before

analysis to convert dissociated free acetate ions (CH3COO-)

to acetic acid (CH3COOH).

RESULTS

Exponential Feed Rate

To generate baseline data against which other cultivations

could be compared, a batch cultivation using a conventional

exponential feeding algorithm was conducted. The algo-

rithm determined the appropriate glucose feed rate at any

time according to an assumed constant yield coefficient and

a desired specific growth rate by the following equation:

Feed rate X0A

Y

et 5

Where X0 = biomass inoculated (g); A = specific growth rate

desired (h1); Y = biomass yield coefficient (g biomass.g

substrate1); t = fermentation time (h).

Baseline data were obtained using the following

parameters: X0:1.2 g; A: 0.2 h1; and Y: 0.4 gg1. Due

to the presence of complex nutrients, the actual specific

growth rate was higher than the desired specific growth rate

and varied between 0.5 and 0.2 h1 (Fig. 3). Residual

glucose in the reactor was maintained at f1 mM and a

final biomass concentration of 33 g (DCW).L1 was

produced. Despite the relatively low residual glucose level,

acetate accumulated during the fermentation, reaching high

concentrations of up to 48 mM by the time biomass growth

had ceased (Fig. 2).

Microscopy indicated that cells were suspended in the

early stages of the cultivation with no tendency for floc

formation, while after 4 h [6 g (DCW).L1] the culture

began to form long filaments, which developed into large,

fast-settling flocs by 6 h [12 g (DCW).L1

]. The large flocsremained for the rest of the cultivation. Cell growth after

floc formation was almost linear even though glucose was

supplied exponentially (Fig. 2).

Modification of Exponential Feed Rate

To better match the supply glucose according to cell

requirements, a combination of exponential and constant

feeding was used, with exponential feeding until hour 6,

Figure 4. Cultivation profile from slow linearly fed trial fed at a linear feed rate of 20 mM glucose.h1 for the entire cultivation. Total glucose added

(mM) (.), residual glucose in the reactor (mM) (o), acetate (mM) (5), biomass [g (DCW).L1] (E).



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followed by a constant feed rate of 44 mM glucose.h1 for

the rest of the cultivation to match the linear growth

pattern. Despite being fed glucose at a slower rate than the

baseline trial, acetate accumulated with no extra gain in

final biomass concentration (Fig. 3).

Constant Feed Rate

Feeding the culture at a constant rate of 20 mM.h1 for the

entire 70-h cultivation caused a temporary acetate accu-

mulation over the first 20 h (150 mM), but avoided the high

final levels of acetate and resulted in a final biomass

concentration of 49 g (DCW).L1 (Fig. 4). This indicated

that the present strain was capable of growing to high cell

density when the acetate was kept low during the latter

stages of exponential growth.

Implementation of the Starvation-BasedDO-Transient Controller

To assess the effectiveness of the starvation-based

DO-transient controller for minimising acetate accumu-

Figure 5. Measured dissolved oxygen transients in response to feed interruptions in DO-transient trial with a starvation level of 2%. The solid lines at the

top of the figure indicate feed addition. DOT(1) is calculated immediately before the feed interrupt and DOT(2) is calculated at the end of the interruption

period. The new feed rate, based on the comparison of DOT (1) and DOT(2) is then implemented for the next period.

Figure 6. Cultivation profile from 2% DO-transient trial, which had a constant starvation level of 2%. Total glucose added (mM) (.), residual glucose in

the reactor (mM)(o),acetate (mM) (5), biomass (g (DCW).L1) (E).



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lation, a trial was conducted using a constant imposed

starvation level of 2%. This meant that in order to

increase the feed rate the controller required at least a

2% decrease in DO-transient in response to the 30-sec

feed interrupt.

Over the first 3 h of the 2% DO-transient trial, the culture

responded with an immediate increase in DO as soon as the

feed flow was interrupted, indicating that glucose limitation

was achieved using our algorithm (DOT(1); Fig. 5).

Immediately after resumption of the feed, a decrease in

oxygen concentration was evident as the microbial uptake

of oxygen again resumed in order to metabolise the

available glucose (DOT(2); Fig. 5). This trial successfully

kept the residual glucose concentration below 2 mM and

produced 37% more final biomass than the baseline trial

but acetate still accumulated quickly after hour 5 (Fig. 6).

To further control acetate accumulation, a second DO-

transient trial was conducted using an increased starvation

level of 5%. This trial produced a final biomass density of

50.8 g (DCW).L1 (Fig. 7), an improvement of 60% more

than achieved in the baseline trial. The acetate concen-

tration remained low over the first 9 h of cultivation, but

Figure 7. Cultivation profile from 5% DO-transient trial, which had a constant starvation level of 5%. Total glucose added (mM) ( .), residual glucose in

the reactor (mM) (o), acetate (mM) (5), biomass [g (DCW).L1] (\scale 125%E).

Figure 8. Cultivation profile from 27% DO-transient trial, which had an increasing starvation level, initiated at 2% and increasing by 1% for every

100 mM glucose added. Total glucose added (mM) (.), residual glucose in the reactor (mM) (o), acetate (mM) (5), biomass [g (DCW).L1] (E).



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again accumulated over the remainder of the cultivation,

reaching levels greater than 60 mM. The growth profile

of this trial suggested that an even tighter control of

feed supply was necessary, especially towards the end of

the cultivation.

Accordingly, a third DO-transient trial was carried out in

which the imposed starvation was initially set at 2%.

Thereafter, for every 100 mM of glucose added it was

increased by an additional 1%, reaching a maximum of

7% by the end of cultivation. For example, after 100 mM of

glucose was added, the starvation level was increased

23%, after 200 mM from 34%. The resulting 27% DO-

transient trial produced 37% more biomass than the baseline

trial, and despite slightly increasing concentrations after

hour 9, acetate was maintained below growth inhibitory

concentrations for the entire cultivation. As expected, the

lowered glucose-loading rate resulted in a higher average

biomass yield at the expense of slower growth as less

substrate was wasted as acetate (Fig. 8). Even though a

lower final biomass concentration was achieved (20% less

glucose was added to the culture) this culture was deemed

more suitable for induction due to the lower accumulated

level of acetate.The sensitivity of this controller to changing conditions in

the reactor was demonstrated with regard to the addition of

antifoam. The antifoam had an inhibitory effect on the

metabolic activity of the bacteria, which was evident from an

immediate change in the response to feed interrupts (Fig. 9).

Directly after the addition of antifoam, the DO-transient

controller made several No decisions (indicating that

the culture was no longer starved) and the feed rate was

decreased accordingly until the culture had recovered,

13 min later.

DISCUSSION

General Discussion of Behaviour ofDO-Transient Controller

The key aspect for achieving efficient industrial E. coli

cultivation is to provide feed at a rate that guarantees a

maximum growth rate while maintaining acetate levels

below inhibitory concentrations. Such a feed rate is reached

when feed is only supplied to limit the microbial activity; in

other terms: when a measurable feed demand is established

and the culture exhibits a minimum level of starvation.

In line with previous work (Kleman et al., 1991;

Konstantinov et al., 1990; Konstantinov and Yoshida,

1990; Shiloach et al., 1996) and theoretical considerations,

the point when a culture becomes feed-limited (starved) can

be established from a decrease in its oxygen uptake rate.

Using very short 30-sec feed interrupts, decreases in

bacterial oxygen uptake rate could be detected online and

equated to the onset of starvation. This knowledge could

then be exploited to feed at a level that kept the culture

continuously feed limited. As the transition from substrate

saturation to substrate limitation is gradual, there is noabsolute value that indicates either saturation or limitation.

Hence, an adjustable setpoint was necessary that allowed

presetting the level of starvation (feed demand) to a value

that prevented acetate accumulation. In our study, this

setpoint parameter was the starvation level tolerated

between DO-transients after a 30-sec feed interruption.

The DO-transient controller successfully limited the

amount of residual glucose in the reactor to less than 2 mM

(Fig. 7). This did not, however, prevent the production of

acetate. By increasing the starvation setting from 2 7%

Figure 9. Effect of antifoam addition on dissolved oxygen undulations early in Constant starvation trial. Arrow indicates point of antifoam addition.

Directly after antifoam addition, the DO-transient controller made several No decisions and the feed rate was decreased accordingly until the culture had

recovered 13 min later.



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over the course of the cultivation (Fig. 8), the residual

glucose was lowered to 0.5 mM, which significantly

reduced acetate accumulation (Table II).

Antifoam

The starvation-based DO-transient controller continuously

monitors the current starvation level of the culture and

supplies glucose at a rate that is consistent with a desired

starvation level. The effectiveness of the controller fordetermining starvation level is clearly illustrated in

Figure 9. Initially, the cultures response to feed inter-

ruption is clear and well defined and resulted in a Yes

decision and an increase in feed rate. After the addition of

antifoam (indicated by the vertical arrow) the culture

response to feed interruptions are poor and five No

decisions are made with concomitant decrease in feed rate

before the culture recovers its capacity for respiration.

While the physiochemical effects of antifoam addition

cannot be entirely excluded, the biological response to

antifoam is evident from the disruption of the characteristic

DO response to feed interruptions. If the effect had been a

purely physiochemical one, we would expect the biological

response to be unchanged except for the absolute DO

concentrations (i.e., higher if OTR is enhanced or lower of

OTR is reduced). The disappearance of the effect within a

short time is a further indication of it being a short-term

biological inhibition.

The addition of antifoam lowered the respiratory activity

of the culture for about 13 min. Unattended, this effect

would be likely to result in the relative oversupply of

glucose and the onset of glucose fermentation to acetate

and undesired culture behaviour. This example shows that

the DO-transient controller has the capability to deal with

temporary culture disturbances by lowering the feed supplyas requested by the culture.

Comparison of DO-Transient Controller With OtherFeedback Controllers

DO Stats

DO stats provide feed when a sudden increase in DO

indicates that the culture has degraded the previous batch

aliquots of added substrate and a new aliquot of feed is

needed. The problem with this method is that it provides an

environment where the culture is saturated with feed forconsiderable periods, during which acetate accumulation

may occur. Konstantinov and Yoshida (1990) present an

interesting DO controller where feed is interrupted and DO is

monitored to determine the moment of its jump, signalling

glucose depletion. The time taken for the jump in DO to

occur was termed the MGA (marker of glucose accumu-

lation) and the feed rate was dynamically manipulated to

provide a constant MGA over the cultivation. Typical values

were about 10 sec; thus, the culture is effectively maintained

at a glucose-saturated level (i.e., at any time the culture has

10 sec worth of glucose present in the reactor). The MGA

was automatically measured every 30 min. In contrast, our

starvation DO-transient controller maintains the culture

under strict glucose limitation most of the time, as a control

decision is made every 2.5 min and feed supply is only

increased as long as the culture is feed limited.

Comparison of DO-Transient Controllers in Defined

and Complex MediaAkesson et al. (2001) originally presented a promising

basis for the development of feed controllers utilising DO

transients. This controller executed preprogrammed subtle

increases and decreases in feed rate and used the resulting

change in DO to judge whether more or less feed needed

to be provided over the next interval. The method worked

well when applied to recombinant cultures of E. coli

grown in defined media (Akesson, 1999). Mixed results

have been reported using this strategy on complex media.

Ramchuran et al. (2002) applied this approach successfully

with lower concentrations of complex and amino acid

supplemented feeds; however, the same strategy workedless effectively when applied to cultures of recombinant E.

coli grown in media with a high complex carbon

component because of difficulties in reoxidising acetate

(Johnston et al., 2003).

Controllers that apply tempered changes to the feed rate

(e.g., 25% increase or decrease) generate fainter response

signals than those caused by the complete interruption of

feed used in this study. By contrast, small DO changes in

response to substrate depletion are not easily detected in

complex media, as the cells continue to utilise the complex

components (Lee, 1996). A stronger response signal is

advantageous as it extends the usefulness of the controller

to function effectively in complex media.

The complete suspension of feed rate provided by our

controller allowed shorter feedback control intervals

(2.5 min) compared to the previously described method

(8 min). A shorter loop time in the DO-transient controller

allows more frequent and smaller feed changes, which

allows closer monitoring of the culture. While the

previously described controller (Akesson et al., 2001)

worked well in defined media, our controller may offer ad-

vantages over others suggested for complex media (Johns-

ton et al., 2002, Johnston, 2003) and in situations where

frequent control loops are necessary (e.g., culture upset due

to antifoam addition).

Principle of Acetate Overproduction

Conventional exponential feed algorithms are designed to

maintain growth below a critical specific growth rate,

assuming that at faster growth rates acetate accumulation

will occur. In line with other presentations (Akesson et al.,

2001; Majewski and Domach, 1990; Holms, 1996), our

approach assumes that acetate accumulation is principally



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the result of glucose uptake rates being higher than

consumption rates of glycolysis metabolites (TCA cycle)

forming a bottleneck. The growth rate is an indirect and not

necessarily reliable indicator of this phenomenon.

Comparison of Experimental Results to ComputerModel Predictions

To explain the behaviour of our control algorithm on a

pure mathematical basis, it was combined with the most

essential algorithm of microbial growth (Monod kinetics).

The Monod model predicts only glucose uptake and

growth and not metabolic bottlenecks, and hence there is

no acetate excretion. The model consisted of the following

kinetic algorithms:

r rmax S

kS S6

A Y r 7

Where r = specific glucose uptake rate (mmol.g1.h1);

rmax = maximum specific glucose uptake rate (4 mmol.

g1.h1); kS = half saturation constant (0.5 mmol.L1); S =

Figure 10. Modelled data showing opposing glucose (solid line) and oxygen (dashed line) concentration oscillations characteristic of experimental data.

Figure 11. Modelled data with various starvation levels; 1% (), 2% (.), 4% (w), 8% (E), and 16% (5). Despite maintaining the same algorithmthroughout the cultivation, the culture always showed an increasing residual glucose concentration towards the end of the cultivation time.



11/12

glucose concentration (mmol.L1); A = specific growth rate

(h1); Y = yield coefficient (0.072 g of biomass formed per

mol of glucose).

General Model Behaviour

As expected, the model showed that repeated short feed

interruptions resulted in the pattern of DO oscillations

typical of the E. coli experiments (Fig. 10). The fact that

glucose levels also oscillated significantly questions the

precision of glucose sampling. The model confirmed

experimental findings that a higher starvation level resultedin lower residual glucose concentrations and longer

fermentations times.

Effect of Growth Over Short Intervals of Time

A culture undergoing exponential growth will exhibit

increasing levels of oxygen uptake, even over short time

intervals of 30 sec. This is especially critical at high cell

density. For example, at a given doubling time of 32 min

the maximum oxygen uptake rate of the culture would

increase by 1.55% every 30 sec. If this increase is not

observed after a 30-sec feed interruption, indications arethat the culture is already feed-limited, meaning that even

using an imposed starvation level of 0% would maintain the

culture at a slightly starved level.

Possible Reasons for Acetate Excretion Towards theEnd of the Fed-Batch

It was interesting to note that the simple model showed

an increasing residual glucose concentration over the

cultivation time, even though the algorithm that determines

the feed rate was the same. This is in accordance with

experimental findings that acetate accumulated in the latter

stages of exponential growth. This phenomenon can be

explained by the fact that increasing concentrations of

biomass can more quickly deplete the given glucose

concentration over the 30-sec feed interruption, resulting

in greater OUR oscillations at higher cell densities (Fig. 11),

which the DO-transient controller interprets as an indica-

tion of starvation. This purely mathematical shortcoming in

our feeding algorithm could be limited by using an

improved algorithm that automatically adjusted the starva-

tion level to the increased biomass levels by multiplying itwith a biomass factor (0.1 * X g.L

1). The improved DO-

transient controller avoided glucose build-up towards the

end of the culture (Fig. 12), but not the increased glucose

fluctuations. These modelling results explain why gradual

increases in the percentage of imposed starvation from

2 7% in the laboratory controller produced the best results.

For a future improved version of the DO-transient

controller, this concept seems worth considering.

The authors thank Wayne Johnston for assistance in developing the

program in LabViewR and useful discussion.

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Figure 12. Modelled data with biomass adjusted starvation level. The build-up of residual glucose towards the end of the cultivation is prevented.



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