Enzymes in Biomass Conversion - American Chemical Society

Chapter 10

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Role of Statistically Designed Experiments in the Development of Efficient Downstream Processes R. C. Lawson and Κ. W. Evans Cell Technology, Inc., 1668 Valtec Lane, Boulder, CO 80301

The following are required for the development of efficient downstream processes. 1) Integration of the fermentation with the downstream processing. 2) Understanding of how each step in the process affects the other steps. 3) Making each step as robust to incoming sample variations as possible. 4) Consistently producing maximum quality product from each fractionation step. This requires the process has clear goals and objectives, the starting material and the final product are well defined in a measurable way and the affect of each processing step on the product is well understood. The use of statistically designed experiments to make the process robust will be discussed. Several types of fractionation techniques for recovery and the future direction of downstream processing will be outlined.

This paper will focus on the use of statistically designed experiments to develop effective purification processes in the most time and cost efficient fashion. Downstream processing and the recovery of proteins by several different techniques have been discussed in other articles (1-3) and will not be discussed here. Typical biotechnology purification processes have steps such as fermentation, separation of cellsfromthe growth medium, purification of the product of interest and storage of the product. In each step of the process there may be several factors (eg. salt concentrations, flow rates, pressure, time and temperature) that are key to the proper operation of that step in the process. If there are 7 steps, each with 3 important factors that can be run at a high or low level there are 2 =2.1xl0 different possible combinations to evaluate in determining the most efficient process. This number doubles if one wants to repeat the results. Obviously, this number of experiments is cost prohibitive and impractical to conduct. Through the use of statistically designed experiments there are ways to obtain the required information from a significantly fewer number of experiments. The use of statistically designed experiments has been outlined in several books (4-6) and specifically for application (7x3)

0097-6156/91/0460-0123$06.00/0 © 1991 American Chemical Society

In Enzymes in Biomass Conversion; Leatham, G., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1991.

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in biotechnology by Haaland (6). Classes on the subject have been offered by several organizations (7-9). In statistically designed experiments, parameters are changed intentionally to determine how the responses change. In this way, the factors (variables) that affect 1) the location of the mean of the responses, 2) the variation around the mean of the responses, 3) the mean and the variation, or 4) have no effect on the responses, can be identified. The designed experiments, through the use of orthogonal, fractional factorials, also allow for the determination of interactions (synergistic effects) between factors. The factors that are resistant to changes in the other factors (robust) can also be determined by these types of experimental designs. With these data, a process which meets the goal of being on target with the least amount of variation can be developed. The use of full factorial designs (all possible combinations) allows for the complete definition of the system. However, this approach is usually resource demanding and cost prohibitive (ie. too many experiments need to be completed). The use of fractional factorial designs allows for the identification of the important factors (the needed information) for only a fraction of the resources. Fractional factorials are a defined fraction of the full factorial design. For example, in a one half fractional factorial design, one half of the number of runs of the full factorial design would be completed. Through the use of balanced (orthogonal) designs, the appropriate fraction of the full factorial can be completed so that the desired information can be obtained from the experiment An orthogonal design is a set of experimental conditions in which the levels of each of the factors is balanced over the other factors (10). Table I illustrates an orthogonal array in which each factor has a high (1) and low (0) setting at which the experiment can be run. Table I: An example of an orthogonal design run

Factor A Factor Β Xi x 2

1 2 3 4 5 6 7 8

0 0 0 0 1 1 1 1

0 0 1 1 0 0 1 1

Factor C x 0 1 0 1 0 1 0 1

Responses 3

yi y2 y3 y4 y5 y6 y7 y8

With the above design each run is an experiment performed at the settings noted in columns X X , and X , with each run having a response (yl - y8). For example, if one were looking for enzymatic activity, the responses y 1 to y8 would be in units of activity per mg of protein. Notice that for factor A at the low levels (0), the levels for Β and C are the same as the levels when A is at its high level (1), i.e. the design l9

2

3



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is balanced (orthogonal). When the responses for each run where A was at its low setting are averaged and compared to those averaged when A was at its high setting the effects of the other changing variables (B and C) cancel out. In other words, by subtracting the mean of the responses for when A was at its low setting from the means of the responses when A was at its high setting, the effect of A on the process can be determined without interference from the effect of Β and C. This difference is commonly scaled (by dividing by 2) and referred to as the "half effect". The half effect for each single factor (A, B, C), each two factor interaction (AB, AC, BC), and the three factor interaction (ABC) can be determined by this method (11). The design also allows for determination of how factor A is affected by the changes in the other factors. If there is a small variation around the mean value when A is at the low setting then the low setting of factor A is considered resistant to changes in the other factors (robust). If the value is also close to the target value then the signal to noise ratio (S/N) will be high (72). Data obtained from statistically designed experiments such as these lead to the rapid development of robust and efficient processes. Statistically designed experiments have been important in determining optimal plating efficiencies of primary breast tumor cells for in vitro assays (13). Glacken, et.al., used statistically designed experiments in modeling mammalian cell culture systems to optimize and help overcome some of the barriers to scale-up of the system (14). Statistically designed experiments have also been used by Dunn (15) to explore ligand protein binding. In the manufacturing of enzymes for biomass conversion, these techniques would be directly applicable to maximizing yield and specific activity along with minimizing costs and time for the manufacturing through optimization of the process. A few examples of other areas of interest in biomass conversion that could benefit from the use of designed experiments would be in determining the mechanism of action of a specific enzyme, determining the synergistic affects between enzymes, cofactors and/or reaction conditions, and determining an efficient immobilization or crosslinking procedures. Because our business deals with biopharmaceuticals, the examples in this paper will come from that field. However, the techniques can be used wherever there is a need to efficiently gather informationrich data. In this paper, we report the use of experimental design in the development of a large scale manufacturing process for ImuVert, a biologic response modifier. ImuVert is currently in human clinical trials for the treatment of human tumors. ImuVert is derivedfromSerratia marcescens and is a combination ofribosomesand lipid vesicles derived from the cell membranes of the bacterium. The activity of ImuVert has been demonstrated in both in vitro (16-17) and in vivo (human clinical) studies (Jaeckle, K. A. / . Clin. Oncol., in press). Our goal is to have an efficient manufacturing process that produces a high yield of a highly active product with the least amount of lot to lot variation. The objective is then to determine how each of the important factors in the process affect the yield and composition of the product. We have used full and fractional factorial designs to determine the important individual factors and two factor interactions for each unit operation. As a result, we have been able to set operating limits for the efficient functioning of the operation. The process steps to be discussed are fermentation medium development and cell lysis


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development. development.

We also discuss the future of experimental design in process


Materials and Methods ImuVert Manufacturing. The cells were harvested from the fermentation medium and washed by centrifugation, lysed with a French pressure cell and the cell debris was removed by centrifugation. The cell lysis supernatant was then layered on a sucrose gradient and the ImuVert pelleted by centrifugation. The pellet was then resuspended in buffer A (see below) to a nucleic acid concentration of 2 mg/mL. Fermentation procedure. Twelve hours prior to the inoculation of the fermenter, 1 L of medium was inoculated with a 2 ml glycerol stock of Serratia marcescens. Bacterial cultures were grown at 36°C for 12 hours. Thirteen liters of medium was sterilized for 20 min. at 121°C in a 20 L Chemap fermenter. The medium was cooled to 36°C and maintained at that temperature throughout the fermentation. The pH was maintained at 6.9 with 5M NaOH. The dissolved oxygen concentration was maintained at the prescribed level by agitation RPM adjustments (min. 400, max 700 RPM) and the addition of air/0 . The glucose concentration was maintained between 10 to 20 g/L. A 3.6% v/v inoculum of the 12 hour preculture was added to the fermenter and the fermentation was monitored hourly for optical density at a wavelength of 660 run, glucose concentration and wet weight of cells per liter. The culture was allowed to grow for 5 hours and then stopped by rapidly cooling the culture to 0°C by running the culture through a stainless steel cooling coil packed in an alcohol/ice bath. 2

Media. Vitamins and trace metals (Factor A, experiment 1) were added at 0.5 mL/L from the following stock, choline chloride 10 g/L (Sigma), pantothenic acid 0.5 g/L (Sigma), riboflavin 0.18 gA (Sigma), biotin 0.1 g/L, folic acid 0.5 g/L (Sigma) and niacin 2.75 g/L (Spectrum) along with 5 mL/L of the following stock, ZnSO^F^O 0.22 g/1 (Sigma), CaCl 0.55 g/1 (Sigma), M n C V ^ O ° - & (Sigma), FeS0 0.5 g/L (Sigma), ( N H ^ M o A ^ H p 0.1 g/L (Sigma), CuSO^HaO 0.16 g/L (Sigma) and CoCl 0.16 g/L (Sigma) or yeast extract 10 g/L (Marcor) was used. Amino acids (Factor B, experiment 1) were supplied as casein peptone 35 g/L (Marcor) or casamino acids 25.8 g/L (Marcor). Dissolved 0 (Factor C, experiment 1) was maintained at either 10 - 20 % or 20 - 30 %. Buffers and trace elements (Factor D, experiment 1) were either NaH^PC^ (Spectrum, USP) at 1.53 g/L, Na HP0 (Spectrum, USP) at 19.5 g/L, ¥Ά^Ο (Spectrum, USP) 0.74 g/L, NaNI^HPC^ (Spectrum, USP) 1.11 g/L, and ΜφΟ+ΊΥΙβ (Spectrum, USP) at 0.15 g/L or Kosers citrate (Difco) at 12.3 g/L Glucose concentration was maintained by addition of 50% w/v glucose solution (Spectrum, USP). 5

2

4

2

2

2

4

Α

Buffer Α. 0.02M MgS0 (Spectrum, USP), 0.05M NI^Cl (Spectrum, USP) and 0.02M Tris-hydroxymethyl aminomethane (Spectrum,USP), pH 7.6 at 4°C. 4



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Assay procedures. Vesicle toribosomeratio measurements. The membrane vesicles (about 200 nm) were separated from theribosomes(about 25 nm) by gel filtration chromatography. Toyo Soda TSK-75 Toyopearl (Supelco) was packed to a bed height of 20 cm in a 1.5 χ 50 cm column (Bio-Rad) at a flow rate of 3-4 mL/min with 3-4 column volumes of Buffer A. The flow rate was adjusted to 2 mL/min and one column volume of buffer A was run through it. A 0.5 mL sample of 2mg/mL RNA final product was loaded on the column and run at 2 mL/min. The effluent was monitored at a wavelength of 280 nm and recorded on a strip chart recorder. The vesicle toribosomeratio was calculated by dividing the peak height of the vesicle peak by the peak height of theribosomepeak. The acceptable range for ImuVert is 0.75 + 0.15. Determination of nucleic acid yield. Cleared lysate or ImuVert was diluted to a concentration which had an absorbance between 0.4 and 0.5 at 260 nm and the absorbances at 280 and 260 nm were measured. The nucleic acid concentration in solution was then calculated by the method of Warburg and Christian (18). The yield of nucleic acid was calculated by determining the percent of nucleic acid in the lysate that was isolated in the product. The acceptable range for ImuVert manufacturing is 10.5 + 1.5. Results Example number 1. Medium for fermentation. The goal of this experiment was to develop a defined fermention medium that was usable for the manufacture of ImuVert. The objectives were to evaluate the effect of replacing citrate with a phosphate buffer, yeast extract with vitamins and minerals, casamino acids with casein peptone and high or low 0 levels on the final yield and composition of ImuVert. 2

Experimental Design 1. Due to the large scale of each run, an experimental design with a minimum number of runs was employed to obtain the desired information. The one half fractional factorial in Table Π was set up to determine the effect of each factor. Table Π: Medium Component Experimental Design FACTOR C FACTOR A FACTOR Β run O. casein/casamino vitamin/veast 1 hi vitamin casein peptone 2 yeast extract casein peptone hi 3 vitamin casamino hi 4 hi yeast extract casamino 5 vitamin casein peptone lo 6 yeast extract casein peptone lo 7 vitamin casamino lo 8 veast ext. casamino lo hi = 20 to 30% disolved oxygen, lo = 10 to 20% dissolved oxygen. a

a

FACTOR D buffer phosphate citrate citrate phosphate citrate phosphate phosphate citrate



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Figure 1 illustrates the half effect results for the vesicle to ribosome ratio and the yield of nucleic acid in the product. The half effect is a measure of the change in the response as a function of the variable under study. For example, a half effect of one for yield would represent a change of two percent in the yield (eg. 10 to 12%). Half effects of less than 0.7% for yield and 0.06 for vesicle toribosomeratio are considered not significant. The largest effects on the yield were the change in the amino acids, the change in the buffers and the two factor interaction (synergistic effects other than the two effect added together), AB. The percent dissolved oxygen or the changingfromyeast extract to vitamins and minerals did not significantly affect the yield, as single factors. For the vesicle toribosomeratio the largest effects were the change from yeast extract to vitamins and the two factor interactions, AC, CD and BC. Changing the dissolved oxygen percent, the buffers or the amino acid types did not significantly affect the vesicle toribosomeratio, as single factors. From these data it can be concluded that the combinations of individual factors (interactions) were important concerns in ImuVert production along with the single factors yeast extract or vitamins (for vesicle toribosomeratio), amino acids, and buffers (for yield). In this particular experimental design, the two factor interactions were confounded with another two factor interaction and a deconfounding experiment was required to resolve the effects. As an example of a deconfounding experiment, the following full factorial (Table ΙΠ) was run to illustrate how to resolve the AB interaction from the CD interaction (ie. the affect of combining yeast extract or vitamins and minerals with the different amino acid types from the affect of combining the different dissolved oxygen percents with the different buffer types). In this case, the A and Β factors (yeast extract and casein peptone) were held constant and the C and D factors (percent dissolved oxygen and buffers) were varied.

Table ΙΠ: Medium Component Deconfounding Experimental Design

a

FACTOR C FACTOR D run Q2 buffer 1 hi* phosphate 2 lo phosphate 3 hi citrate 4 lo citrate hi = 20 to 30% dissolved oxygen, lo = 10 to 20% dissolved oxygen.

The results of a deconfounding experiment that distinguishe between the AB and the CD interaction were displayed in Figure 2. These data indicate that the interaction between the percent dissolved oxygen and buffers (CD) was important for the vesicle to ribosome ratio (Figure 2b) and not for the yield (Figure 2a). Since the CD interaction is not important to the yield the interaction between yeast extract and amino acid types (the AB interaction in the first experiment) must have been important for the yield. In this way, the affect of each interaction on the responses can be determined. Confirmation of the results obtained in the above experiments was obtained by completing two additional runs using yeast extract, caseine peptone, kosers citrate and


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Yield

4-> υ


Φ

(β

χ

Β

C

D

AC

AB

BC

CD

BC

Factors Vesicle-Ribosome

Ratio

AC Factors

Figure 1. Half Effect Results for Yield (A) or Vesicle to Ribosome Ratio (B). A is the effect of vitamins and yeast extract. Β is the effect of casein peptone and casamino acids. C is the effect of dissolved 0 - D is the effect of citrate and phosphate. AB is the interaction of yeast extract/vitamin and mineral with casein peptone/casamino acids. A C is the interaction of yeast extract/vitamins and minerals with dissolved O-. BC is the interaction of casein peptone/casamino acids with dissolved L> . CD is the interaction effect of dissolved 0 with the citrate or phosphate. 2

2

2


130


Yield

υ Downloaded by UNIV OF CALIFORNIA SAN DIEGO on June 8, 2015 | http://pubs.acs.org Publication Date: April 30, 1991 | doi: 10.1021/bk-1991-0460.ch010

Φ

ι Η

CD

D Factors

Vesicle-Ribosome

Ratio

•P

υ

Φ 4-1

ο

Η

Figure 2. Half Effect Results for Yield (A) or Vesicle to Ribosome Ratio (B) deconfounding experiment. C is the effect of type of buffer on the sysem. D is the effect of dissolved 0 on the system. CD is the interaction of dissolved 0 with the buffers. 2


2

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20 to 30 % dissolved oxygen. The results were a vesicle toribosomeratio of 0.65 ± 0.05 and a yield of 9.4 ± 0. The components identified as being critical by this design have put us on target with minimal variability in the system (see the assay procedures section of the methods for the acceptable limits for the responses). Example number 2. Cell Lysis with a Manton-Gaulin Press. The goal was to find a scalable lysis procedure for the manufacturing of ImuVert. The objective was to determine how changes in pressure (6000 to 10000 psi) and the number of passes (1 to 10) of the bacterial suspension through the Manton-Gaulin homogenizer affect the final specifications for ImuVert. The solution exiting the press was collected in a flask that was in an alcohol/ice bath and constantly stirred to cool the solution rapidly. Experimental design 2. The experiment that was conducted as a full factorial with three levels.This design consisted of 9 experiments plus a control constituting a set of manipulations which could be completed in one day with minimal resource allocation. A three level design tests for nonlinear responses while a two level design assumes that the response is linear between the high and low set points, see Table IV.

Table IV: Lysis Experimental Design Run 1. 2. 3. 4. 5. 6. 7. 8. 9.

FACTOR A passes 1 1 1 5 5 5 10 10 10

FACTOR Β pressure 6000 8000 10000 6000 8000 10000 6000 8000 10000

Figure 3 displays the results obtained from the Manton-Gaulin experiment. The graphs illustrate that the largest effect on both the yield and vesicle toribosomeratio was the number of times the solution was passed through the homogenizer and the response was nonlinear. The signal to noise ratio was plotted on the same graphs to illustrate which of the responses was closest to the target with the least amount of variation. The larger the signal to noise ratio the closer the data was to the target value with the least variation in the data. In this way, the S/N was useful in deciding which conditions were appropriate to consistantly product a high quality product Figure 4 illustrates the interaction between the number of passes and pressure for the responses yield and vesicle toribosomeratio. If there was no interaction between these two effects then the lines would be parallel. However, at the higher pressures (8000 and 10000 psi) and 10 passes the increase in the yield was greater than at the lower pressure (figure 4a). At 10 passes the 10000 psi pressure the increase in the vesicle toribosomeratio was greater than at the lower pressures (figure


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0

2

4

6

PASSES

8

10

12

5000

8000

11000

PRESSURE

Figure 3. Single Factor Effect Plots and Signal to Noise Ratios. Α. Θ is the effect of passes on vesicle toribosomeratio. A is the signal to noise ratios for the data points. Β. Β is the effect of pressure on vesicle toribosomeratio. A is the signal to noise ratios for the data points. C. ε is the effect of passes on yield. A is the signal to noise ratios for the data points. D. s is the effect of pressure on yield. A is the signal to noise ratios for the data points.



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VESICLE/RIBOSOME 1.2

Figure 4. Interaction Of Pressure With Passes. A, Yield, s is the effect of 6000 psi. A is the effect of 8000 psi. Ο is the effect of 10000 psi. B, vesicle to ribosome ratio. Β is the effect of 6000 psi. A is the effect of 8000 psi. Ο is the effect of 10000 psi.



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E N Z Y M E S IN BIOMASS CONVERSION

4b). This means the interaction between high pressure and high passes was important in the lysis of the bacteria and needs to be considered in the manufacturing of ImuVert. Figure 4a also illustrates that the pressure has only a small effect on the yield at 5 passes. This means that response (yield) was robust to the changes in the pressure at this number of passes and if the only concern was yield then the specification for the pressure settings used in manufacturing could be loosened. However, the vesicle toribosomeratio was not robust to changes in pressure (figure 4b), and the pressure setting that put the preparation on target must be used (6000 psi) if composition was also important. The results of the experiment demonstrate the power of these designs in being able to define the factors which require strict control and which can be relaxed. The conditions closest to the desired specifications were 5 passes at 6000 psi. A confirmation run was conducted separately to verify the appropriate conditions. Table V illustrates the results of the confirmation run and confirms that the condition chosen produces product which is in our target range (see assay procedures) and produces product similar to the standard method used to manufacture ImuVert. Table V: Confirmation Runs Comparing Two Methods of Lysis run French press(control) Mantin-Gaulin

vesicle/ribosome 0.8 09

yield 9.9% 9.7%

Discussion In these experiments we have balanced the resource allocations with the depth of data necessary for each of the processes. In addition, we were able to obtain the necessary information to complete the task efficiently. In classical experimentation, one factor was changed until the optimum was found and then the next set of experiments were done at the new optimum, while changing a second variable. This procedure continued until all the variables were "optimized". With classical experimentation, the true "optimum" was rarely found. This was because only a limited number of experiments were done at each level which did not adequately explore the possible solutions, and therefore, the possibility of missing the true optimum was high. In the experiments described in this study, the interactions were extremely important and may have been missed using a traditional approach. The above examples underscore the need for designed experiments with their ability to determine how each factor affects the system, and how each of the other factors interact with that individual factor. This type of experiment also provides information on the direction to follow if the product is not close enough to its target value after thefirstround of experiments. In other words, the graphs provide insight into the direction to proceed to reach the target of interest. The data also indicate which factors had the largest effect (the



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fastest way to reach the target) and those that were robust (resistant) to changes in the other factors. As suggested by Haaland (79), in using these statistically designed experiments, we employed problem solving techniques (defining the problem with clear goals and objectives), gathered information-rich data (designed experiments) and used the analysis of the data to design the next set of experiments. In this way, we have used the available resources in an organized and efficient fashion. The future direction of designed experiments in a biotechnology setting is two fold; 1. To examine an entire process from beginning to end (raw material suppliers to stability of the final product) as a unit operation. With this frame of reference, the important factors in the entire process and their effect on each other can be determined. The use of initial screening experiments on each of the individual steps would identify the important factors to include in the overall design. This is the only practical way to determine how the process is going to react at the high and low set points for each of the important factors in the process while the other important factors are at either their high or low set points. 2. To use the information gained from these types of experiments to model the process. The model can then be optimized and the tolerances set with the only requirement being computer time and confirmation experiments. The large number of experiments that would be required to perform the tolerance design study can be eliminated by this procedure. Acknowledgments. We would like to thank Rose Lopez, Ralph Yuhase, and Todd Janes for their help in preparing the many samples of ImuVert needed for these studies and Drs. Fred Pearson, Mike Hindahl, Greg Hirschfield, Mr. Dave Smiley and Ms. Connie Phillips for critically reading the manuscript Literature Cited 1. 2. 3. 4. 5. 6. 7. 8. 9.

Scawen, M. D.; Hammond, P. M . J. Chem. Tech. Biotechnol. 1989, 46, 85-103. Wheelright, S. W. J. Biotechnol. 1989, 11, 89-103. Guide to Protein Purification; Deutscher, M . P., Ed.; Methods in Enzymology; Academic Press: San Diego, CA, 1990; Vol. 182. Barker, T. B. Quality by Experimental Design; Marcel Dekker: New York, NY, 1985. Schmidt, S. R.; Launsby, R. G. Understanding Industrial Designed Experiments; CQG Printing: Longmont, CO, 1988. Haaland, P. D. Experimental Design in Biotechnology; Marcel Dekker New York, NY, 1989. Quality Engineering by Design - The Taguchi Approach; The Center for Quality and Applied Statistics, Rochester Institute of Technology: Rochester, NY, 1989. Understanding Industrial Designed Experiments; Launsby and Associates, Colorado Springs, CO, 1989. Strategy of Experimentation; DuPont Specialty Services: Wilmington, DE, 1989.


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Schmidt, S. R.; Launsby, R. G. Understanding Industrial Designed Experiments; CQG Printing: Longmont, CO, 1988; p 3-3. Barker, T. B. Quality by Experimental Design; Marcel Dekker: New York, NY, 1985; pp 198. Barker, T. B. Engineering Quality By Design - Interpreting the Taguchi Approach; Marcel Dekker: New York, NY, 1990; p 20. Besch, G. J.; Tanner, Μ. Α.; Howard, S. P.; Wolberg, W. H.; Gould, M . N. Cancer Res. 1986, 46, 2306-2313. Glacken, M . W.; Fleischaker, R. J.; Sinskey, A. J. Biotechnol. Bioeng. 1986, 28, 1376-1389. Dunn, G. Stat. Med. 1988, 7, 805-815. Warren, R. P.; McCall, C. Α.; Urban, R. W. Mol. Biother. 1989, 1, 145-151. Warren, R. P.; McCall, C. Α.; Urban, R. W. Mol. Biother. 1989, 1, 323-327. Warberg, C. Biochem. Z. 1942, 310, 348. Haaland, P. D. Experimental Design in Biotechnology; Marcel Dekker: New York, NY, 1989; p 15.

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Enzymes in Biomass Conversion - American Chemical Society

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