培养基优化方法

Journal of Industrial Microbiology & Biotechnology (1999) 23, 456–475 Ó 1999 Society for Industrial Microbiology 1367-5435/99/$15.00 http://www.stockton-press.co.uk/jim Strategies for improving fermentation medium performance: a review M Kennedy and D Krouse Industrial Research Limited, PO Box 31–310, Lower Hutt, New Zealand Many techniques are available in the fermentation medium designer’s toolbox (borrowing, component swapping, biological mimicry, one-at-a-time, statistical and mathematical techniques—experimental design and optimization, artificial neural networks, fuzzy logic, genetic algorithms, continuous fermentation, pulsed batch and stoichiometric analysis). Each technique has advantages and disadvantages, and situations where they are best applied. No one ‘magic bullet’ technique exists for all situations. However, considerable advantage can be gained by logical appli- cation of the techniques, combined with good experimental design. Keywords: medium design; medium optimization; fermentation; gamma-linolenic acid; neural networks; fuzzy logic; genetic algorithms Introduction When developing an industrial fermentation, designing a fermentation medium is of critical importance because medium composition can significantly affect product concentration, yield and volumetric productivity. For commodity products, medium cost can substantially affect overall process economics. Medium composition can also affect the ease and cost of downstream product separation, for example in the separation of protein products from a medium containing protein. There are many challenges associated with medium design. Designing the medium is a laborious, expensive, open-ended, often time-consuming process involving many experiments. In industry, it often needs to be conducted frequently because new mutants and strains are continu- ously being introduced. Many constraints operate during the design process, and industrial scale must be kept in mind when designing the medium. Some of these con- straints and challenges are summarized in Table 1. In Michael Crichton’s fiction book, The Andromeda Strain [36], ‘The Wildfire project employed almost every known growth medium’, totaling 80 in all. If only this were true! A medium design campaign can involve testing hun- dreds of different media. One of the more difficult aspects of the medium design process is dealing with this flow of data. In reality, often the information generated from design experiments is difficult to assess because of its sheer vol- ume. Beyond about 20 experiments with five variables it is very difficult for a researcher to maintain medium compo- nent trends mentally, especially when more than one variable changes at a time. Data capture and data mining techniques are crucial in this situation. Correspondence: Dr M Kennedy, Industrial Research Limited, Gracefield Rd, PO Box 31–310, Lower Hutt, New Zealand Received 24 December 1998; accepted 18 August 1999 Two different improvement strategies: open and closed Most of the studies published about medium improvement start with the objective of ‘given these components of the medium what is the best combination possible?’. This can be referred to as a ‘closed strategy’ in that it defines a fixed number of components and the type of components used. This is the simplest situation. The disadvantage of this strat- egy is that many different possible components, which are not considered, could be beneficial in the medium. It con- siders an extremely limited subset of design possibilities. It assumes you have chosen the right components to start with. The obverse situation, the ‘open strategy’ asks, ‘What is the best combination of all possible components avail- able?’. This situation is much more complex and difficult to deal with. Experimental design strategies do not handle this situation easily. The advantage of the open strategy is that it makes no assumptions of which components are best. The ideal design strategy would be to start with an open strategy, and then move to a closed strategy once the best components have been selected. Too often researchers pro- gress too quickly to a closed strategy. Three issues are particularly important to consider before medium design starts: the effect of medium design on strain selection; how well will shake flask medium design data scale up; and what is the target variable for improvement. The effect of medium design on strain development Medium design is intrinsically linked to strain development and the two processes form a ‘Catch-22’ circle (you can’t choose the best strain until you have the best medium, and you can’t design the best medium until you have the best strain). This is because no one medium works best for all the strains being tested. Therefore the question arises, which medium should be used to choose strains. Two options are available. The first is to use the best medium composition based on the results with one strain and then zhangjianguo 高亮 zhangjianguo 高亮 Fermentation medium performance M Kennedy and D Krouse 457Table 1 Constraints and challenges that may operate during the process of designing an industrial medium Encountered in laboratory Encountered on an industrial scale I Development time I Availability of raw materials throughout the year I Cost of development efforts I Transport costs of medium components I Lack of shaker space I Batch to batch variability of complex medium components I Precipitation reactions I Medium cost and price fluctuations of medium components I Water quality I Stability of the supply company I Dispersion or dissolution of solid components I Bulk storage and handling of medium components I Effect of components on assay techniques I Pest problems I Effect of components on downstream product purification I Effect of components on broth viscosity or power consumption I Foaming I Disposal costs of spent medium I Dust hazards choose the best strain based on this medium. The second option is to choose the strain based on one general medium and then optimize the medium for the best strain. With both these methods there is no guarantee that one of the dis- carded poor performing strains would not surpass the chosen best strain if a different medium was used. Instead of doing one-at-a-time development (medium then strain, or strain then medium), considerable benefit can be gained by conducting medium design and strain devel- opment simultaneously. An example is given in the study of gamma linolenic acid (GLA) production by Mucor hiemalis IRL51 [69]. GLA is an omega-6 fatty acid, which is a component of triglycerides within oil accumulated within the fungi. The oil content of the cell and GLA content of the oil are linked on a maximum GLA productivity curve (Figure 1). By using this generic relation, new strains can be screened on three media (A, B and C in Figure 1) that locate microbial performance on different parts of this curve, giving a picture of strain performance over a wide range of conditions. Figure 1 The maximum GLA productivity curve for Mucor hiemalis IRL51. Individual points correspond to tested strains and different media. The expected performance of new strains on media can be located along the curve, demonstrating that three media (A, B and C) are sufficient to gauge microbial performance in screening trials. Data are from Kennedy et al [69]. The scalability of shake flask results No matter which medium improvement strategy is chosen, a large number of experiments are usually involved. This large number of experiments necessitates shake flasks, as it is not practical to do large numbers of experiments in stirred controlled vessels. Shake flask systems suffer from at least four weaknesses [70]: the pH is not controlled dur- ing the fermentation; the oxygen transfer capabilities of shake flasks is poor; considerable evaporation can take place during shake flask culture; and shake flasks can lack adequate mixing. Many researchers assume that the best medium chosen from shake flask data will be the best medium in a large- scale stirred tank. The reality is that few rigorous compari- sons of medium performance at different scales have been published, and often, on scale-up, the medium composition is changed to take advantage of control strategies, eg the fed batch addition of substrates. For gamma linolenic acid production one scale-up study [70] showed that, using the same medium, biological performance in 10-L fermenters is usually the same as that in shake flask culture (Figure 2). There were some inconsistencies, which could be attributed to scale, but no large, systematic differences were apparent. Considering the immense amount of data reported on shake flask systems, this is comforting, but does it hold true for all systems? Figure 2 A comparison of medium performance in shake flasks (SF) with that in stirred, pH-controlled 10-L vessels. The performance charac- teristic is GLA content of the oil produced by M. hiemalis IRL51. Symbols show individual results and means for each of six media (a–f), and, as a summary, the overall mean. These results validate the scalability of shake flask results, at least for the system studied. Data are from Kennedy et al [70]. g Shake flask; h 10-L vessels; G SF mean; j 10-L mean. zhangjianguo 高亮 zhangjianguo 附注 利用不确定的培养基筛选不确定的菌种 zhangjianguo 高亮 zhangjianguo 附注 摇瓶试验的缺陷 zhangjianguo 高亮 Fermentation medium performance M Kennedy and D Krouse 458 Target variable Some medium design studies flounder because the target variable to be improved is not clearly defined. In the pro- duction of GLA any of the following performance indi- cators could be chosen for improvement: GLA content of the oil (%); GLA content of the cell (%); GLA concen- tration in the fermenter (g GLA L- 1); triacylglycerol con- tent of the oil (%); specific productivity of GLA (g GLA g cell- 1 h - 1); volumetric productivity of GLA (g GLA L- 1 h - 1); cost of nutrients/unit GLA ($ g GLA - 1); cost of nutrients/unit productivity ($ L h- 1 g GLA- 1). Any of these criteria may be used. For example if the microbial oil is viewed as a competitor for evening prim- rose oil (EPO), which contains 10% GLA, then a higher GLA content of the oil may be desired, eg 15%. If separ- ation of the GLA from the oil to produce purified GLA is required, then as high as possible GLA content may sig- nificantly reduce purification costs. If GLA is thought of as a commodity good then volumetric productivity may be the important variable. It is essential to choose the right target before beginning the design process. Lexography of medium design A word of caution should be expressed about the use of the terms ‘optimize’, ‘optimization’ or ‘optimum’. In the mathematical definition optimization means ‘the maximiz- ing or minimizing of a given function possibly subject to some type of constraints’ [81]. This implies an objective function that is the target to maximize or minimize, and a possible set of constraints. Under this definition, it is impossible to claim to have the optimum medium, as it is always possible that another, as yet unknown, medium could out-perform the so-called ‘optimum medium’. How- ever in the literature many researchers use the term loosely to mean ‘the best medium they have come up with to date’. A better way to describe the medium would be to call it an improved medium, or a medium with enhanced perform- ance. Improvement strategies and procedures Literature search (‘borrow someone else’s medium’) Often the first step is to look and see what media others have used to grow the same genus, species or strain. Several handbooks have been devoted to microbial media [8,9]. The problem with this approach is that usually there are too many options, and too much effort is required to test them all. Experience becomes a key factor in assessing published media. For example many published media are laboratory media that can be discarded as an industrial option because they contain a number of expensive components. Sorting out the published media to come up with a shortlist is essential. Some chemically defined media contain a large number of components eg chromatium medium contains 34 compo- nents. Some antibiotic media contain five carbon sources. Some media contain unusual components (Table 2) which are usually related to the substrate the microbe was isolated from, and emphasize a lack of identification of the nutrient Table 2 Some unusual microbial media components in the published literature Blood products (from sheep, horse, Lima beans guinea pig) Eggs Rabbit dung Prunes Commercial rabbit food Leaf litter Quaker oats Hay infusion Calf brain infusion Lard Carrots and tomatoes Guar gum Bile Cocoa shell requirements of the organism. Some published media suffer from component overloading, which can lead to interac- tions, precipitations or toxic levels of different components. The ‘magic component’ can exist in some media. This is a situation where, for no apparent reason, one component seems to perform much better than other equivalent compo- nents. Usually these components are complex, and can even be specific brands of the same component, for example corn steep liquor. These logic-defying components argue for an extensive ‘open strategy’ prior to focussing on the closed strategy. Component swapping (‘try everything strategy’) One strategem is to take one medium composition and swap one component for a new one at the same incorporation level. This strategy is often used to compare components of one type, eg to compare many different carbon or nitrogen sources [53,104,112]. It is an open strategy and has the advantage that it enables large numbers of components to be compared. It is one of the few open strategies available. Component swapping, however, performs poorly as a total improvement technique because it does not consider component concentrations or interaction effects. It can be best thought of as a screening tool, not so much to find the best medium but to discard the poor performing medium components. It is however, a powerful and useful technique for assessing and understanding microbial regulation. Much of our understanding of carbon, nitrogen, and phosphorus source regulation has been built up from careful consider- ation of such experiments [27,28,87]. Biological mimicry (‘match and win strategy’) The biological mimicry strategy is based on the concept that the cell will grow well in a medium that contains everything it needs in the right proportions. It is simply a mass balance strategy. The composition of the cell, the concentration of cell mass, cell growth yields, and the desired extracellular product concentrations are used to cal- culate how much of the various components should be in the medium [41,43]. It can be performed on two levels, an elemental level, eg balancing the carbon or nitrogen levels, or a molecular level, eg balancing amino acid or phosphate levels. This approach is popular with more complex cell types, or even for developing whole insect diets where the medium should mimic the cell composition of the host or the insect itself. When no medium has worked to date, or there is no obvious starting point for design, this strategy can have a place. Conducting the mass balance is some- thing that, in itself, is useful because it enables the theoreti- zhangjianguo 高亮 zhangjianguo 附注 觉得我们应该明确 zhangjianguo 高亮 Fermentation medium performance M Kennedy and D Krouse 459 cal limiting nutrient to be identified and changed if desired. Utilizing yield data also enables a maximum theoretical final cell concentration to be predicted. In essence, the mass balance is a useful check to determine if the medium is in the right ‘ball park’ in terms of nutrient levels. The mass balance methodology has some significant limitations. Firstly a detailed elemental composition of the cell is required (Table 3). The elemental composition of a cell can vary quite significantly depending on whether the organism to be grown is a yeast, bacterium or filamentous fungus, the stage of growth, if the culture sporulates, or sometimes simply at the species level. The published data usually apply to common organisms such as Escherichia coli, Saccharomyces cerevisiae, Klebsiella aerogenes or Pseudomonas sp. If the organism under test is different from the species from which the published data were obtained, then you are entering uncharted waters using these data. One solution is to grow some of the desired cells and conduct an elemental analysis. While this overcomes prob- lems with uncertainty in cell composition, cell yield data must still be measured. Measuring cell yield on many dif- ferent elements is expensive. laborious and time consum- ing. Such detail is not usually gone through unless the data will have a significant impact and the project is long-term. Next, the elemental composition of the nutrients must be determined. With pure compounds, this is simply a matter of calculation, but with complex, poorly characterized medium components, eg fishmeal or cotton seed meal, the data can be hard to come by. On top of this, the elemental composition of complex medium components can vary Table 3 Elemental composition of microorganisms, and growth yields related to macro- and microelements from Ertola et al [43]. Such data are useful in mass balance calculations for estimating nutrient levels and a maximum theoretical cell concentration Element Elemental composition of Growth yield (g dry microorganisms biomass g element - 1) C 44–53a,b,c 1.1d N 10–14a 8.75d 7–10b,c 9.09e P 2.0–3.0a 39.1d 0.8–2.6b 0.4–4.5c 27.7c S 0.2–1.0a 333d 0.01–0.24b 0.1–0.5c K 1.0–4.5a,b 59.5d 0.2–2.5c 161.3e Mg 0.1–1.2a,b,c 430d 128e Ca 0.01–1.1a,b 3.3 · 103 d 0.1–1.4c 8.3 · 102 e Fe 7 · 10 - 3–0.9a,b,c 6.7 · 103 d 1.7 · 103 e Zn 8 · 10 - 3–2.4 · 10- 2 a,b 2 · 104 d 2.7 · 105 e Mn 7 · 10 - 4–4.8 · 10- 2 a,b 2 · 104 d 7.7 · 105 e aBacteria; byeasts; cfilamentous fungi; dassumed values for Klebsiella aer- ogenes; ecalculated values for a Pseudomonas sp as determined by con- tinuous culture. widely from batch to batch, or on a seasonal basis, eg if a different fish species is used to make fishmeal. If the bal- ance is done on the molecular level, eg, balancing amino acid levels, this technique can lead to nutrient over- or under-loading as cells construct or consume cell compo- nents. Lastly, balancing the elements does not guarantee success. A well-balanced medium can still perform badly. Simply balancing the medium elemental composition does not address the cell’s regulatory machinery, which can dic- tate substrate preferences. One at a time (‘keep it simple’) The rationale behind the one-at-a-time strategy is to keep the concentration of all medium components constant except one. The concentration of this medium component is then changed over a desired range. This strategy has the advantage that it is simple and easy. Most significantly, the individual effects of medium components can be seen on a graph, without the need to revert to statistical analysis. The technique has some major flaws; interactions between components are ignored, the optimum can be missed completely, and it involves a relatively large number of experiments. Because of its ease and convenience, one-at-a-time has historically been one of the most popular choices for improving medium composition [3,47,61,111,113,120,128]. Experimental design (‘maths and stats’) Fisher [45] developed the basic theory of experimental design which shows that changing more than one factor at a time can be more efficient than changing only one factor at a time. Applications to medium improvement date from the 1970s and many studies claim substantial improvements over media obtained using ‘one-at-a-time’. For example, Silveria et al [130] compared ‘one-at-a-time’ and experi- mental design for optimizing the medium composition for Methanosarcina b