26 BioPharm JULY 2002

Process Development

be established between recombinant andcellular protein biosynthesis, then stable andprolonged recombinant protein production ispossible. Extending recombinant proteinformation is the key to increased productyields. Five periods of doubling are neededto achieve maximum specific content. Thespecific content numbers can be derivedfrom the kinetics of the recombinant proteinproduction (4). Host cells, by providing theprotein synthesis machinery, buildingblocks, and energy for the production ofrecombinant proteins, are critical to theprocess. Those factors have not always beenfully considered in the past.

An Integrated ApproachApplying an integrated system that includesaspects of host metabolism, processengineering, and recombinant vectormodification can increase the efficiency(optimize) and accelerate the developmentprocess. The strategy we employ uses strongand regulated host vector systems, whichpermit continuous tuning of the recombinantgene expression rate in relation to themetabolic potential of the host cell. Weshow the feasibility and efficiency of thisapproach by producing a model system ofrecombinant human superoxide dismutaseusing an E. coli T7 host vector system.

The key to maximum cell factoryexploitation is in determining the highestratio between cellular and recombinant geneexpression. Also important are the specificsolutions that prevent runaway plasmidreplication at high gene expression rates andthe strategies that allow tuning of therecombinant gene expression. Weestablished analytical methods to monitorthe metabolic load exerted by recombinantgene expression to enable recombinantprotein production within tolerable loadlimits set by the host cell metabolism.

Undesirable increases in plasmid copynumbers (PCNs) at high recombinant gene

Optimizing Recombinant MicrobialFermentation ProcessesAn Integrated Approach

A new strategy for controllingrecombinant gene expressionimproves efficiency, maximizeshost vector exploitation, reducescosts, improves productconsistency, and acceleratesproduct development. Continuousfeeds of limited amounts ofinducer proportional to biomassgrowth grant optimal control overthe ratio between gene expressionand host cell metabolism,providing stable, prolongedrecombinant protein production.

Process development of bacterial hostvector systems for recombinant DNAtherapeutic products is expensive, timeconsuming, and stringently regulated.Those factors affect a product’s time-

to-market and the producing company’scompetitiveness. The average capitalizedcost of developing a human pharmaceuticalproduct is about $300 million. Developmenttime for therapeutic biopharmaceuticalsincreased continuously over the past fourdecades, from less than four years in 1960 tomore than 10 years since 1989 (1).Consequently, the average patent protectiontime is reduced.

Development is further complicatedbecause each part of the process must becarried out in a defined, sequential order.Each development phase must meet certaincriteria before the next phase can start.Procedures have to be defined early in theprocess because later steps, such asdownstream processing and clinical trials,depend on the availability of sufficientrecombinant protein of reproducible quality.Therefore, short-term set-up of an optimizedproduction process is a key goal inbioprocess development.

Difficulties in Process OptimizingProcess optimization for biopharmaceuticalproduction has traditionally focused ongenetics-based solutions. Strong expressionsystems (such as the T7 system) havetypically been used to increase yields ofrecombinant protein (2). However, suchsystems often override host cell metabolismcausing cell damage or death (3). Because ahost cell’s protein synthesis can bemaintained for only a short time, theproduction phase is shortened and maximumyield is unattainable.

Host cell metabolic capabilities. Processoptimization needs to adapt recombinantgene expression rates to the host cellmetabolic capabilities. If an equilibrium can

Monika Cserjan-Puschmann,Reingard Grabherr, Gerald Striedner, Franz Clementschitsch, andKarl Bayer

Monika Cserjan-Puschmann is a postdoctoralfellow, Reingard Grabherr is an associateprofessor, Gerald Striedner and Franz Clementschitsch are PhD students, andcorresponding author Karl Bayer is a professor andgroup head of microbial fermentation at theUniversity of Agricultural Sciences, Institute ofApplied Microbiology, Muthgasse 18, A-1190 Vienna,Austria, +43.1.36006.6220, fax +43.1.369.7615,bayer@mail.boku.ac.at, www.boku.ac.at/iam/fermand www.boku.ac.at/iam/acbt.

BioPharm JULY 2002 27

expression rates are prevented by modifyingthe replication system. To circumventcomplex and tedious analytical proceduresfor monitoring PCNs, we used amathematical model. We establishedstrategies to regulate recombinant geneexpression by modulating the transcriptionrates with a continuous supply of limitedamounts of inducer.

Material and MethodsOur bacterial host strain was E. coli(HMS174(DE3) from the Institute ofApplied Microbiology collection). We alsoused plasmids (pET11ahSOD andpET11ahSODclone3) from the samecollection (4–6). The coding region ofrecombinant human Cu/Zn superoxidedismutase (rhSOD) is equivalent to thenucleotide sequence of humancomplementary DNA (cDNA).

Growth conditions and media. We used a 20-L (12-L net volume) computer-controlledbioreactor (MBR Wetzikon, Switzerland)equipped with standard measurement andcontrol units, including pH, pO2,temperature, and rpm. We maintained pHcontrol at setpoint 7.0 by adding potassiumhydroxide (10% w/v), foam formationcontrol by adding 5% antifoam suspension(Glanapon 2000, Bussetti, Vienna), andmaintained cultivation temperature at 37 °C.Process control and visualization usedLookout software, version 3.8 (NationalInstruments Inc., www.ni.com). Thefermentation methods we used and thenutrient media are described in the“Fermentation Media” sidebar.

Analytical MethodsWe measured optical density (OD) at 600 nm and determined bacterial dry matter(BDM) by centrifuging a 10-mL cellsuspension, which was then resuspended indistilled water, centrifuged again, dried at105 °C for 24 hours, and reweighed. Wedetermined guanosine tetraphosphate(ppGpp) using ion-pair high performanceliquid chromatography (HPLC) to quantifythe metabolic load on the host cells (7). Wederived PCNs from the ratio of the amountof genomic DNA to that of plasmid DNA,which we quantified by capillaryelectrophoresis (8). We determined thecontent of the rhSOD by enzyme-linkedimmunosorbent assay (ELISA) (9,10).

Colony forming units (CFUs) were used todetermine the number of plasmid-containingcells after 24 hours of cultivation on nutrientbroth (NB) agar plates and on platescontaining 100 �g/mL of ampicillin. Toconfirm an overproduction of rhSOD in theplasmid-carrying cells, we counted CFUsafter induction by isopropylthiogalactoside(IPTG) (at 200 �g/ml) on plates containingampicillin. We considered nonviable cells tohave overexpressed rhSOD and, therefore,to have lost the ability to form colonies.

Monitoring the Metabolic LoadMetabolic load is a complex variableresulting from a number of dynamicbiochemical reactions, which makes it

Fed-batch fermentation with an exponential substrate feedprovides a growth rate of ��0.1 h-1 during 3.5 or 4.0 doublingtimes. Batch volume was four liters at the start of the exponentialphase (when cells enter the stationary phase); exponential feedwas also started at this point. Feed control was achieved byincreasing the pump speed according to the exponential growthalgorithm, x � xoe�t, with superimposed feedback control ofweight loss in the substrate tank. A deep frozen (�80 °C)working cell bank (1 mL, OD600 � 1) was transferred asepticallyto the bioreactor for inoculation.

The nutrient medium composition was semisynthetic (containingtryptone and yeast extract) for batches and entirely synthetic forfed-batch cultivation. Three and six grams per liter of potassium

dihydrogenphosphate and di-potassium hydrogenphosphate•3H20 were added to the medium. Other components were addedin a ratio to the grams per bacterial dry matter (BDM) produced:0.10 g tryptone (only used in batch), 0.05 g yeast extract (onlyused in batches), 0.25 g sodium citrate, 0.10 g magnesiumsulfate•7H20, 0.01 g calcium chloride•2H2O, 50 �L trace elementsolution, 4.00 mg copper chloride•2H2O, 3.20 mg zincsulfate•7H2O, 0.45 g ammonium sulfate, 0.37 g ammoniumchloride, and 3.30 g glucose monohydrate. Trace elementsolution was prepared in 5 N HCl (gL-1) and contained 40.0 FeSO4•7H2O, 10.0 MnSO4•H2O, 10.0 AlCl3•6H2O, 4.0 CoCl2, 2.0 ZnSO4•7H2O, 2.0 Na2MoO2•2H2O, 1.0 CuCl2•2H2O, and 0.50 H3BO3. Glucose and ammonium saltswere autoclaved separately (7).

Fermentation Media

Figure 1. Standard fed-batch process for the production of recombinant human superoxidedismutase using E. coli (HMS174(DE3)pET11ahSOD) with a growth rate of � � 0.1 h-1

maintained by exponential feed

ppGpp (�mol/g BDM)

Induction PCN

total g BDM

total g rhSOD 5qP 5 (mg rhSOD/g BDM,h)

300

250

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100

50

0

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otal

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otal

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5, a

nd q

P 5

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pp

28 BioPharm JULY 2002

Process Development

difficult to quantify. Overexpressedrecombinant proteins exert a metabolicburden on host cells, triggering endogenous,nutrient-starvation response signals.Representative data can be derived formetabolic load, therefore, by using keyvariables of the E. coli cell’s starvation,regulatory, and signal processing networks.

Lengeler’s model organizes metabolic controlhierarchically on three levels — operons,modulons, and stimulons (11). Operons areinvolved in the control of single enzymesand enzyme clusters, and modulons controlthe coordination of functional metabolicunits. On both levels, the availableinformation is very specific, which means

operons and modulons are of only limiteduse in process optimization. However,stimulons are more complex regulatoryentities: They are involved in cellular signalprocessing. Stimulons act on the highestlevel of metabolic regulation bycoordinating widely distributed genes andglobal regulatory networks to redirectenergy consumption and to confer resistanceagainst adverse conditions.

The stringent response network, with itssignal and effector molecule ppGpp, isinvolved in starvation and stress responses.Therefore ppGpp is a promising targetmolecule for monitoring metabolic load. Toquantify ppGpp and relevant energymetabolism nucleotides (such as adenine andguanine), we combined an HPLC methodwith a rapid sampling procedure (to copewith the short half-life of the analytes) (7).

Monitoring aptitude. Figure 1 shows ppGppaptitude for monitoring metabolic loadsderived from recombinant gene expression.Fermentation of our model protein, rhSOD,in fed-batch fermentation of our E.coli strainand plasmid pET11ahSOD, shows thetypical behavior of an expression systemthat is too strong. The expression rate (qP)exceeds the host cell’s capacity, so thesystem collapses (see the path of the totalbiomass in Figure 1). As mentioned,maximum yields are unattainable becausethe biosynthesis cannot be maintained forsufficient time.

The concentration of ppGpp tightlyfollows the increase of recombinant proteinqP after induction. Analogous experiments(data not shown) using a vector expressionsystem with a less strong host (E.coli JM105pKK hSOD) also resulted in increasedppGpp levels after induction, but to asignificantly lower extent, corresponding tomuch lower protein production rates (7).

Figure 2. Modeling of plasmid copy number (PCN) from online data uses neural networksimulations. Fed-batch fermentation of E. coli (HMS174(DE3)pET11ahSOD) has anexponential feed of ��0.2 h-2.

PCN model

InductionStart feed

PCN real

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100

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012 14 16 18 20 22 24 26 28 30 32

Fermentation Time (h)

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eal /

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Continuous addition of inducersignificantly improves bioprocessoperations by:

• fine tuning the recombinant geneexpression rate,

• maintaining the productivity of the hostvector system over a prolonged periodof time, and

• extending the production ofrecombinant proteins.

Inducer Improvements

Figure 3. Fed-batch process for the production of recombinant human superoxidedismutase using modified E. coli (HMS174(DE3)pET11ahSODclone3); shows stabilizedplasmid replication; exponential substrate feed with a growth rate of 0.1 h-1; inducer feed(�mol IPTG/g BDM) was increased in three steps to assess stability of the plasmid copynumber: 0.6–1.17 at 35–43 h, 1.17–3.26 at 43–48 h, and 3.26–19.6 at 48–55 h.

ppGpp�200 (�mol/g BDM)

PCN

total BDM(g)

total rhSOD 2 (g)

qP(mg rhSOD/g BDM,h)

50

45

40

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034 36 38 40 42 44 46 48 50 52 54 56

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, and

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00

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30 BioPharm JULY 2002

Process Development

However, even significant increases ofppGpp, assumed to trigger stringentresponse mechanisms, could not protect thehost cell metabolism from beingoverstrained and finally collapsing. Thatcould be a result of very high transcriptionrates for the target mRNA by the T7 polymerase, which curtails the synthesisof mRNAs for the translation of cellularproteins (7). The behavior suggests thatrecombinant gene expression rates must becontrolled during transcription.

Plasmid ReplicationFigure 1 shows the dramatic PCN increaseat high expression rates. Two antisenseRNAs, RNA II (which acts as a primer,initiating duplication of DNA strands) andRNA I (which binds to RNA II beforepriming, inhibiting duplication), effectivelylimit plasmid replication (5,12).

Uncharged transfer RNAs (tRNAs)diminish the inhibitory effect of RNA Ibecause of their extensive homologysequence to RNA I and RNA II, whichinterferes with plasmid replication control.At high recombinant gene expression rates,uncharged tRNAs are elevated throughincreased amino acid and by shifted demandfor individual amino acids. High tRNAlevels increase plasmid replication.

The resulting increased gene dosagestrongly stimulates recombinant geneexpression because T7 polymerase binds toits own promoters, enhancing transcriptionrates. Higher transcription rates trigger achain reaction that leads to metabolicoverburden for the host cell.

Model for PCN estimates. Process optimizationalways approaches the limits of the individualhost vector system by operating fermentationprocesses at high qP, and the plasmidreplication rate always increases under theseparameters. Because plasmid replication isintrinsically coupled to the level of host cellmetabolites, our process operation focus wason gaining access to plasmid replication

regulators. The mathematical model wedeveloped for monitoring and estimatingPCNs is based on a neural network. We usedfermentation data sets obtained online (CO2production rate, alkaline–base consumption,and OD) and offline (PCN data sets of fiveidentical fermentations) to train the neuralnetwork simulations. To create the self-organizing maps (SOMs), we used ViscoverySOMine (Eudaptics Software,www.eudaptics.com) (13).

Figure 2 shows the aptitude of the modelby comparing real and modeled PCN datafor a standard fed-batch fermentationprocess. The modeled PCN data even showa significant increase of PCN afterinduction. Nevertheless, we used apublished analytical method for determiningPCNs for the experiments shown in Figures 3, 4, and 5 (8). Although modelingaccelerated and improved data acquisition,we were unable to abolish the undesirablePCN increase at high recombinant geneexpression rates using process controlmeasures. Therefore, to prevent theharnessing effect from excessive plasmidreplication, we targeted our modifications onthe origin of the replication.

Strategies for PCN stabilization were derivedfrom the plasmid replication scheme ofColE1. Because overreplication is mainlytriggered by the binding of unchargedtRNAs to either RNA I or RNA II, ourstrategies focused on restoring thereplication control function of replicationcontrol. Such strategies must be designed sothat the homology is maintained between thetwo antisense RNAs but destroyed betweenthe antisense RNAs and the tRNAs. So weexchanged the sequence of seven

Figure 4. Fed-batch process for the production of recombinant human superoxidedismutase using modified E. coli (HMS174(DE3)pET11ahSOD) with a continuous feed ofinducer (IPTG) to control transcription rate

ppGpp�200 (�mol/g BDM)

cfu 108

PCN

qP (mg rhSOD/g BDM,h)

total rhSOD (g)

45

40

35

30

25

20

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10

5

0

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100

50

020 22 24 26 28 30 32 34 36 38 40 42 44

Fermentation Time (h)

Tota

l rhS

OD

, qP,

and

cfu

108

ppG

pp 2

00 a

nd P

CN

Type of Process Total Yield of Recombinant Improvement Compared withOptimization Step Protein in Fed-Batch Processa Standard Processb

Standard processb 8.99 NA

Fed-batch process 1c 23.10 2.5

Fed-batch process 2d 33.20 3.6

Fed-batch process 3e 18.97 2.1

aGrams per 10 liters of broth volumebFed-batch with pulsed induction for recombinant gene expressioncUsing gene construct with stabilized plasmid (pET11ahSODclone3) replication and continuousaddition of increasing amounts of inducer (IPTG)

dWith continuous feed of optimized amounts of inducer (0.9 �mol/g BDM IPTG)eWith continuous feed using lactose as the inducer

Table 1. Summary of key results of individual steps of process optimization

32 BioPharm JULY 2002

Process Development

nucleotides in loop two with its complementwithout reversing the sequence (5,6).

The modified E. coli gene construct(HMS174(DE3)pET11ahSODclone3) wasgrown in fed-batch culture at a growth rate of0.1h-1 (Figure 3). By modifying the origin ofreplication, PCN was kept constant at a lowlevel during the entire fermentation processdespite varying recombinant gene expressionrates. With the lower and stable gene dosage,the production phase could be significantly

prolonged to about four doubling times. Fourdoublings increases the yield about 2.5 timescompared with standard processes (Table 1).The significant metabolic load reduction(shown in the ppGpp data) highlights theefficiency of this approach. Stabilizingplasmid replication at a low level improvesthe researcher’s control over recombinantgene expression rate, particularly in tuningthe transcription rate.

Figure 5. Fed-batch process for the production of recombinant human superoxidedismutase using modified E. coli (HMS174(DE3)pET11ahSOD) with a continuous feed ofinducer (lactose) to control transcription rate

ppGpp�200 (�mol/g BDM)

PCN

cfu�108

total BDM (g)

total rhSOD (g)

qP (mg rhSOD/g BDM,h)

35

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, qP,

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cfu

(�

108 )

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CN

, and

ppG

pp�

200

Transcription RegulationStable plasmid replication significantlyimproves recombinant gene expressioncontrol. That response can be used tooptimize development processes. Wedeveloped strategies to adjust qP to the ratenecessary to prevent damage to the host cellsynthesis machinery. Gene expression ratescan be continually regulated by addinglimited amounts of inducer. Using thisstrategy, the transcription rate is directlycontrolled by titration of the repressor. Theinduction regime, however, requires thataccurate amounts of inducer are fed to keepit in a constant ratio with the biomass. Thisis a different process from that typicallyused, which adds inducer in a single pulse.The benefits of continuous additions ofinducer are shown in the “InducerImprovement” sidebar.

Inducer feed regime. To determineappropriate (and tolerable) amounts ofinducer for maximum recombinant geneexpression, we exposed recombinantbacteria to exponentially increasing amountsof inducer, added to the substrate feed of afed-batch culture. By using the experimentalset-up shown in Figure 6, optimal amountsof inducer can be calculated for singlefermentation runs (14). Appropriate amountsof inducer for succeeding runs can beestimated from deviations to the theoreticalgrowth curve triggered by overexpression ofrecombinant proteins (14).

In a series of experiments (data notshown), we determined that host cellmetabolism remains uncompromised byoverexpression of a recombinant gene at anoptimum (tolerable) amount of 0.9 �molIPTG/g of BDM at a growth rate of 0.1h-1.Figure 4 shows the aptitude of that feedregime in combination with the appropriateamount of inducer in fed-batch fermentation.Using optimum exploitation of the hostvector system and extending thefermentation process, recombinant proteinyield is increased 3.6 times when comparedwith standard methods of pulsed inducersupply (Table 1). This process alsosubstantially improves downstreamprocessing by increasing batch-to-batchreproducibility and improving productconsistency (homogeneity) and quality.

In further experiments, we investigatedgrowth rates to determine the dependence ofcritical amounts of inducer (IPTG). Toachieve equal recombinant gene expression

Figure 6. Model-based determination of appropriate amounts of inducer (IPTG) in ratio withthe biomass in a fed-batch process for maximum exploitation of the host cell metabolism

feedstart

inducer dosageinto the media

total IPTG (�mol)

IPTG related to BDM

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34 BioPharm JULY 2002

Process Development

rate at increasing growth rates, the amountof inducer must be increased (Table 2). Thatphenomenon is probably the result of lessefficient inducer transport systems andsubstrates at higher growth rates (15).

Lactose InductionThe novel induction strategy we used allowsIPTG to be replaced by lactose, which is animportant asset for quality assurance inindustrial bioprocessing. Critical points inusing lactose for induction are its transportinto the cell (unlike IPTG) and itssimultaneous consumption. Therefore theobjective is to limit carbon duringcultivation to prevent inducer exclusion andto feed appropriate amounts of lactose toprovide high level recombinant geneexpression.

Using our model (Figure 6), we show thatapproximately 120 �mol lactose/g BDM(two orders of magnitude higher, data notshown) are necessary to achieve inductionrates equivalent that of the IPTG. In carbon-limited fed-batch fermentation of E. coli(HMS174(DE3)pET11ahSOD) withexponential substrate feeds and continuouslactose supply, yield was increased 2.1 timescompared with standard processes (Figure 5). Although this proves the aptitudeof lactose for recombinant gene expression,the synthesis potential of the host vectorsystem was not yet fully exploited (whichcan be seen in the ppGpp data): Room foroptimization still exists. The goal is todesign a sophisticated inducer feed regime atthe optimal balance between lactoseinduction and consumption. Althoughtranscription rate control is more complexwith lactose than with IPTG, lactose offersthe advantage of transcription rates that arerestored to tolerable levels by lactoseconsumption.

Targeted Process OptimizationThe integrated approach we describe isuseful for producing recombinant proteinson an industrial scale and for designingexperiments for targeted processoptimization. Our approach improves unitcosts, increases efficiency, and acceleratesprocess design and operation for producingrecombinant proteins. Control ofrecombinant gene expression guaranteesoptimal exploitation of the host vectorsystem and leads to foreseeable host cellbehavior. Variation of cellular componentsis minimized, facilitating downstreamprocessing.

In addition to industrial applications,regulating recombinant gene expressionoffers a unique tool for investigating cellphysiology for experiments such asdetermining the different metabolic loadsresulting from varying recombinant geneexpression.

Although regulating recombinant geneexpression by controlling transcription ratesis effective, oscillations still exist in plasmidreplication rates. Combining the inducerfeed strategy described and modifyingplasmid replication together with moderngenomic and proteomic tools furtherimproves control and consistency inrecombinant protein production systems,thus accelerating bioprocess development.

References(1) R. Karia, Y. Lis, S.R. Walker, “The Erosion of

Effective Patent Life: An InternationalComparison,” Medicines: Regulation and Risk,J.P. Griffin, Ed. (Queens University Press,Belfast, 1992), pp. 287–302.

(2) F.W. Studier and B.A. Moffat, “Use of theBacteriophage T7 RNA Polymerase to DirectSelective High-Level Expression of ClonedGenes,” J. Mol. Biol. 189, 113–130 (1986).

(3) S.-T. Liang et al., “mRNA Composition andControl of Bacterial Gene Expression,” J. Bacteriol. 182(11), 3037–3044, (2000).

(4) W. Kramer et al., “Kinetic Studies for theOptimization of Recombinant ProteinFormation,” Ann. NY Acad. Sci. 782, 323–333(1996).

(5) R. Grabherr et al., “Stabilizing Plasmid CopyNumber to Improve Recombinant ProteinProduction,” Biotechnol. Bioeng. 77, 142–147(2002).

(6) R. Grabherr, E. Nilsson, and K. Bayer,Expression Vectors with Modified ColE1Origin of Replication for Control of PlasmidCopy Number (EP 00 121709.0) (2000).

(7) M. Cserjan-Puschmann et al., “MetabolicApproaches for the Optimisation of

Recombinant Fermentation Processes,” Appl.Microbiol. Biotechnol. 53(1), 43–50 (1999).

(8) S. Breuer et al., “Off-Line QuantitativeMonitoring of Plasmid Copy Number inBacterial Fermentation by CapillaryElectrophoresis,” Electrophoresis 19,2474–2478 (1998).

(9) K. Bayer et al., “Humane RekombinanteSuperoxiddismutase,” BioEngineering 6(6),24–30 (1990).

(10) T. Porstmann et al., “Rapid and SensitiveEnzyme Immuno Assay for Cu/Zn SuperoxideDismutase with Polyclonal and MonoclonalAntibodies,” Clinica Chimica Acta 171, 1–10(1988).

(11) Biology of the Prokaryotes, J. Lengeler, G. Drews, and H. Schlegel, Eds. (GeorgThieme Verlag, Stuttgart, Germany, 1999).

(12) B. Wróbel and G. Wegrzyn, “ReplicationRegulation of ColE1-Like Plasmids in AminoAcid-Starved Escherichia Coli,” Plasmid 39,48–62 (1998).

(13) E. Dürrschmid et al., “Optimized DataExploration of Recombinant FermentationsUsing Neural Network Simulations,” Proc.ACoFoP IV Intern. (Conference on AutomaticControl of Food and Biological Processes,Göteborg, Sweden), 21–23 September 1998.

(14) G. Striedner et al., “Metabolic Approaches forthe Optimisation of Recombinant FermentationProcesses,” Recombinant Protein Productionwith Prokaryotic and Eukaryotic Cells: AComparative View on Host Physiology(Kluwer Academic Publishers, New York,2001).

(15) A. Death and T. Ferenci, “Between Feast andFamine: Endogenous Inducer Synthesis in theAdaptation of Escherichia Coli to Growth withLimiting Carbohydrates,” J. Bacteriol.176(16), 5101–5107 (1994). BP

Growth Rate IPTG� (h-1) �mol/g BDM

0.1 1.1

0.2 2.3

0.3 4.8

Table 2. Dependence of amount of inducerper gram of biomass dry matter (�mol/gBDM) at different growth rates (�) to gainequivalent recombinant gene expression

Liz Howard, PhD, is an IntellectualProperty Partner with Orrick,Herrington and Sutcliffe in theirSilicon Valley office, not in SanFrancisco as reported on page 21 of“Stem Cells and Xenotransplantation— Ethics, Patents, and Politics: AnIndustry Roundtable” (Volume 15,Number 6). We apologize for theerror.

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