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Fully-Automatic Measurement of Mycelial Morphology by Image Analysis Keith G. Tucker, Tom Kelly, Patricia Delgrazia, and Colin R. Thomas* School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
A fully-automatic image analysis method is described for the comprehensive and quantitative analysis of the morphology of filamentous mycelia grown in submerged cultures. The method not only allows rapid measurement of important morphological parameters on freely dispersed mycelia but also provides a novel characterization of the aggregated (clumped) form. The latter can constitute more than 90% of the biomass in some fermentations and might therefore be expected t o have a major influence on broth rheology, fermenter mixing, mass transfer, and hence fermentation productivity. Clumps are characterized not only in terms of the percentage of mycelia in this form, as in earlier work, but also in terms of clump area, perimeter, compactness, and roughness. The method has been tested on laboratory-scale Streptomyces clavuligerus and Penicillium chrysogenum fermentations. It is not only more comprehensive than previous methods but also faster and thus will permit more extensive physiological and engineering studies on mycelial fermentations than has previously been possible.
Introduction There are many important industrial fermentations involving submerged cultivation of filamentous microorganisms. The mycelial morphology can range between two extremes, pellets and free filaments, the latter being most common. Freely dispersed mycelia in these fermentations are prone to entanglement, which may have an adverse effect on the rheological properties of the broths, resulting in problems with mixing, mass and heat transfer, and possibly loss of productivity (Atkinson and Daoud, 1976). A method of characterizing mycelial morphology which might be used to investigate the relationships between morphology, rheology, and productivity is therefore important if the design and operation of these fermentations is to be improved. Such a method might also be used for physiological studies of mycelial fermentations. Initial investigationsof mycelial morphology relied upon inaccurate and time-consuming manual measurements from photographs. Subsequently,simple digitizing tables were used despite operator dependence and inherent slowness (Metz, 1976;van Suijdam and Metz, 1981; Allan and Prosser, 1983). Typically, the technique involved tracing with a cursor over mycelia in a photograph or projected image on a digitizing table in order that morphological parameters such as main hyphal length, total hyphal length, and number of hyphal tips (Metz, 1976) could be calculated from the recorded coordinates. To quantify the morphology throughout a fermentation, an automated method of analyzing a significant number of mycelia from each sample is desirable. Such a method using image analysis has been developed (Adams and Thomas, 1988;Packer and Thomas, 1990). Although this method was fast (typically 0.2 min/organism), it did not attempt to characterize in detail entangled mycelia (“clumps”)which can form a significant proportion of the biomass (Packer and Thomas, 1990) and which might therefore be expected to influence broth rheology. Furthermore, this method did not allow measurements on individual hyphal branches, by branch order, which may be important in physiological studies, nor did it estimate mean hyphal diameters. 8756-7938/92/3008-0353$03.00/0
A similar method to study the mycelia of streptomycetes in a growth chamber mounted on a microscope stage has also been developed (Reichl et al., 1990). However, this method was not intended for studying samples taken from submerged fermentations, and it is relatively slow, taking 3-15 min to characterize one branched mycelium. The fully-automated method described here allows the most comprehensive analysis of a sample of nonpelleted filamentous mycelia yet possible and at a faster rate than previous methods. It has been tested on samples from laboratory-scale Streptomyces clavuligerus and Penicillium chrysogenum fermentations.
Materials and Methods S. clavuligerus ATCC 27064 was grown in a 7-L stirred tank fermenter (L. H. Fermentation Ltd., Reading, U.K.) using a soluble complex medium (Belmar-3einy and Thomas, 1991). Samples were taken 24 h after inoculation with spores and then a t approximately every 24 h for 5 days. Samples were diluted 400-fold with distilled water, and a 40-pL aliquot was transferred to a slide, air dried, and stained with methylene blue (0.3 g of methylene blue, 30 mL of 95% ethyl alcohol, and 100 mL of water). P. chrysogenum (strain PC8; SmithKline Beecham plc., Worthing,U.K.) was grown in a 6-L stirred tank fermenter (Life Science Laboratories Ltd., Luton, U.K.) a t 800 rpm and an aeration rate of 1w m on the defined medium of Deo ar.d Gaucher (1984). The fermenter was inoculated with a 36-h-old vegetative culture grown in shake flasks. The miilimum dissolved oxygen was maintained at 40 5% air saturation by the utilization of a gas blender. Two batches of glucose (40% w/v) giving initially 20 g/L and finally a total of 35 g/L of broth were added during the growth phase a t 0 h and 30 h, respectively. During the production phase, (40-124 h) glucose (40% w/v) and ammonium sulfate (10 % w/v) solutions were fed continuously to the fermenter at rates of 5.4 mL/h and 4 mL/h, respectively. Sampleswere taken at roughly 12-hintervals throughout the fermentation and immediatelymixed with an equal volume of fixative (13 mL of 403’5 formaldehyde, 5 mL of glacial acetic acid, and 200 mL in 50% v/v ethanol). The sample was then further diluted 20-fold and
0 1992 American Chemical Society and American Institute of Chemical Engineers
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stained with a few drops of lactophenol trypan blue (0.5 g of trypan blue in 100 mL of lactophenol). A small drop of diluted sample was then placed on a slide. A cover slip was gently placed over the drop, to spread out the mycelia and prevent them from migrating around the slide during measurement. The mycelia were confined by this procedure to a volume of only a few micrometers in depth, presenting them in essentially a two-dimensional form. A Polyvar optical microscope (Reichert Jung Opstiche Werke AG, Austria) fitted with an eight-slide automated stage, and autofocus capability was used to analyze the slides at 100 times magnification in the case of S. clavuligerus and 40 times magnification for Penicillium chrysogenum. The microscope was connected to a Quantimet 570 image analyzer via a color video camera (Model XC-007 CCD camera; Sony, Japan). Only the green light channel from the camera was used for this application, giving effectively a monochrome image. Each point (pixel) of this 512 X 512 pixel image had one of 256 grayness levels (between white and black). The interpixel distances when 100times and 40 times magnification were used were 1.09 pm and 2.72 pm, respectively. The Quantimet 570 (Leica Cambridge plc., Cambridge, U.K.) is a general purpose image analyzer, with an extensive range of image analysis routines. These were selected and combined to develop software for morphological characterization of mycelia. Two versions of the software were written. One fulfilled only the tasks of the method of Packer and Thomas (1990)and was written to enable speed comparisons. The other, the full version, permitted several important extensions to the latter method and is described below. Both methods were tested on samples from the S . clauuligerus fermentation. The use of the full version was also demonstrated on samples from the P. chrysogenum fermentation.
Image Analysis Software Before images of microorganisms are analyzed, various program parameters must be set. These are application specific, varying for the particular fermentation being investigated. They have previously been discussed in detail by Packer and Thomas (1990). For example, to optimize the acquired image, the brightness of the microscope lamp and the contrast and gain of the camera must be set. As samples contain dust and debris, it is sometimes possible for small particles to appear attached to hyphae, creating false branches on the mycelia in the image. A minimum acceptable branch size is therefore specified to eliminate most of these events. A calibration value is set to permit the conversion of interpixel distances in images into micrometers. Through automatic stage motion and focusing it is possible to scan up to eight slides in any desired pattern; the scan pattern to be performed on each slide is therefore established at this stage. A measuring frame smaller than the total image is specified. The gap between the frame and the edge of the image is set to the length of the largest mycelium likely to be found in the samples. By only considering objects within the frame, bias due to truncation of mycelia at the edge is avoided. Once all the parameters have been established, many slides can be analyzed without returning to this initial setting-up phase. In the analysis phase the stage moves to the first position on the first slide and performs an autofocus. The image is then captured and stored in digital format (Figure la). This gray image is enhanced by “delineation” which improves the definition of the edges of features by setting pixels to local maximum or minimum gray levels. It is then “segmented”; i.e., grayness levels above the preset
level are chosen as being of interest. A masking binary image is thus defined which shows which objects have been selected for further processing. As the actualgrayness levels of the objects are not needed for morphological characterization, subsequent processing is performed on this binary image. The binary image is subjected to a single “close” operation to consolidate the detection. This first removes outer layers of pixels from the detected objects. Any small particles will be eliminated. Pixels are then added to restore the remaining objects. This operation removes any spots in the binary image caused by inconsistent grayness level of the background and also small dust particles. The remaining objects in the binary image are then split into four groups, i.e., branched hyphae, unbranched hyphae, clumped material, and debris. A description of how this is done follows, and a diagrammatic representation is given in Figure 2. The binary image is reduced by an “ultimate skeletonization”,i.e., exhaustive removal of pixels from the outside of objects until only single pixels or loops of pixels remain. Clumps (aggregated mycelia) contain encircled nondetected (background) regions that reduce down to loops. All other objects reduce down to single pixel “seeds”.The loops can be isolated from the seeds and rebuilt into the clumps using the original binary image as a template. The clumps are then stored in a second binary image and removed from the original image. Lack of branch points can then be used to identify unbranched hyphae, which can also be stored in a further binary image and removed from the original image. The original image now only contains debris too large to have been removed so far and branched mycelia. Large debris is removed by eroding pixels from the outside of the objects until the relatively thin mycelia vanish, rebuilding the remaining objects (large debris) to their original size, and removing them from what still remains of the original image. This leaves branched mycelia and debris of an intermediate size, i.e., of similar area to the mycelia. Both what remains of the original image (containing the branched mycelia) and the stored image of unbranched mycelia still contain debris. This is removed via a “feature accept” that eliminates any object more circular in shape than a preset limit. The mycelia being filamentous in nature tend to be far from circular in shape. In the case of the stored image containing clumps, debris is removed by eliminating any object from the image with an area less than a certain number of pixels. Size rather than circularity is used in this case as a typical clump will be larger in size than the debris but will have a more circular outer perimeter than an individual mycelium. If circularity was utilized as the limiting factor on the image containing clumps, not only would debris be removed, but also some of the microorganisms. As the clumps and unbranched and branched mycelia at this stage have been separated into three individual binary images (Figures lb,c,d),the primary phase of the processing is complete and some initial measurements are now possible. The total area (by pixel count) of the microorganisms in each binary image is used to calculate the percentage of clumps. The images are then taken in turn for measurements of area and perimeter on individual mycelia or clumps. These and other morphological parameters, discussed in detail below, are then recorded in a raw data file. The branched mycelia, however, must be processed further in order to identify individual branches by branch order before characterization. First, a copy of the image containing branched mycelia is subjected to an ultimate skeletonization (similar to that described earlier). The resulting seeds are stored to be
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Figure 1. Photographs showing various images created throughout the program. (top left, panel a) Image captured and stored in digitalformat; (top right,panel b) binary image of clumped mycelia; (bottomleft, panel c) binary image of unbranched mycelia; (bottom right, panel d) binary image of branched mycelia.
used later for rebuilding the main hyphae (zero-order branches) after higher order branches have been recognized and severed from them. Another copy of the branched mycelial image is conventionally"skeletonized", i.e., outer pixels are removed until a single-pixel-width centerline of each hypha is obtained. Any false branches caused by this process or debris touching the mycelia are removed using the parameter preset in the setting-up phase of the software. The resulting skeletons are "pruned"; i.e., pixels are removed from the ends of all the branches. Successive more vigorously pruned versions of the skeletons are compared. Disappearance of a join, where a hypha branches, indicates the pruning back of a branch as far as that join. Such a branch cannot be part of the main hyphaor zero-order branch of that mycelium, which is defined here as having the longest connected path through the mycelium (Packer and Thomas, 1990). It should therefore be severed. This is achieved by using the lost join in a series of image manipulations that result in identifying
the base of the branch. When this is removed from the original skeleton of the mycelium, it severs the branch, as illustrclted in Figure 3. The process operates on all skeletons simultaneously and repeats until there are no branch points left. The stored seeds from the ultimate skeletonization are then used to rebuild the skeletons of the zero-order branches. These are stored elsewhereand removed from the original skeletonized image. This results in a new image which contains only first-order and higher order branch skeletons. This image can be treated in the same way as the first to extract the first-order branches. These are also preserved in a separate binary image. The process is repeated until all the branches up to, and including, the fourth order (if present) have been extracted. The result of these manipulations is a series of images containing zero-order branches, first-order branches, and so on. Measurements on the mycelia are carried out serially. Each main hypha is extracted in turn from the
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A
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Figure 2. Diagrammatic representation of the binary image classification process. relevant image, that of the zero-order branches. It is measured and then dilated (a layer of a few pixels in width is added to the outside) so it can be used as a link to the first-order branches of that mycelium. These can then be taken one at a time, measured, dilated, and explored through successive branch-order images. A t each stage measurements of area and perimeter are carried out and used to determine other derived hyphal characteristics (described below) which are stored in the raw data file. The program, having completed analysis on the first image, moves the microscope stage to the next field in the scan pattern and repeats the whole process until all the fields defined have been analyzed. The program then stores a summary of the measurements taken from that slide in the data file, and the slide is changed, ready to begin scanning again. Once all the slides have been analyzed, the raw data file can be printed. The program also creates a file of data suitable for input into Sigmaplot (JandelScientific, Corte Madera, CA) or a similar plotting program. Apart from the setting-up phase, the program described above is fully-automatic and sometimes classified hyphae as clumps and vice versa in a manner that would not necessarily be acceptable in a manual operation. Slide staining problems can also create artificial breaks in mycelia. For those cases where accuracy is paramount, manual editors are available and can be used to correct the binary images or to reclassify objects. However a loss of speed is a consequence and in many cases the additional accuracy is unnecessary (Packer and Thomas, 1990). Morphological Measurements. For the individual mycelia, the area and perimeter measurements mentioned earlier are used to estimate other morphological parameters. These include the length of the longest connected path through the mycelium (main length); the total length
of all the hyphae in the mycelium; mean hyphal diameter; dimensionless length (main length divided by diameter); and the hyphal growth unit (totallength divided by number of growing tips). Also the length, width, and branch order of individual branches making up the mycelia are calculated. Mycelia in clumps cannot be isolated for independent measurement. However, important measurements can still be made on the clumps. Besides the percentage of mycelia in this form, and the clump area and perimeter (both by pixel count), the circularity factor given by perimeter' 411 X area is calculated as an estimate of clump roughness (hairiness). The clump "compactness" is estimated by two methods: by the ratio of the area of the hyphae in the clump to the total area enclosed by its actual outer perimeter and by the ratio of the area of the hyphae in the clump to its convex area. This latter area is that bounded by the convex perimeter, in which concavities in the clump boundary have been infilled. This is shown diagrammatically in Figure 4. Results Figure 5 shows values for mean total hyphal length, mean main hyphal length, and mean number of tips per mycelia across the time course of the test S. clavuligerus fermentation. These results and those of the other morphological parameters (not shown) agree well with the measurements of Packer and Thomas (1990), on other samples taken from this fermentation. The pattern of fragmentation, reextension, and further fragmentation previously reported (Packer and Thomas, 1990; BelmarBeiny and Thomas, 1991) is clear.
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Branched mycelial skeleton i s eroded,
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Dilated branch point i s combined with skeleton as i t was before t h e branch point vanished.
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Figure 3. Method of branch isolation repeated throughout “shrink back” algorithm operated on the image containing the skeletonized branched mycelia# I 6.0
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Figure 4. Diagrammatic representation of a clump. Lines: perimeter; (- - -) convex perimeter.
Figures 6 and 7 show how the present method provides a more detailed description of the freely dispersed form found in this S. clauuligerus fermentation. Figure 6 gives the proportion of mycelia that are unbranched, have one or more first-order branches (but none of higher order), or have one or more second-order branches. Figure 7 gives the various mean branch lengths by their order. In this particular fermentation, there appear to be no mycelia with branches of third order or higher and only small changes in the branch lengths. The method also provides a more detailed description of clumps than has ever been possible before. Figures 8, 9, and 10 illustrate some of the data for the more highly clumped samples from the P. chrysogenum fermentation. Besides the percentage of clumps, the area, perimeter, compactness, and roughness of individual clumps are also measured. As can be seen (Figure 8), the percentage of clumps quickly rises to over 90% during the growth phase
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, 20
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100 120 Fermentation time ( h ) 80
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Figure 5. Variation in mean total mycelial length, mean main hyphal length, and mean number of tips per mycelium throughout the time course of the test S. Clavuligerus fermentation. The error bars represent the standard error of the means. Symbols: ( 0 )mean total mycelial length; (v)mean main hyphal length; (A)mean number of tips per mycelium.
and remains high for the duration of the fermentation. Figure 9 shows how the clump area changes markedly during the fermentation and how this is reflected in the clump roughness. Figure 10 illustrates how the two measures of clump compactnessvary throughout the time course of the fermentation. Although a similar trend appears in both measurements, the actual values are very different,and the oscillationsin the values calculated using the convex area are much more pronounced. Table I illustrates the speed of the method on samples from the 5’.clauuligerus fermentation: for all the samples using the full program measuring branch orders and
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Fermentation t i m e I h I 24
96 Fermentation time ( h ) 48
72
120
Figure 6. Percentage of all freely dispersed mycelia from a 7-L batch S. clauuligerus fermentation having no branches, firstorder branches, or first- and second-order branches. Bars: (solid) unbranched mycelia; (open) mycelia with first-order but not second-order branches; (hatched) mycelia with second-order branches. 1
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Figure 8. Variation in biomass, percentage of clumps, and mean total mycelial length throughout the time course of the test P. chrysogenum fermentation. Symbols: ( 0 )percentage of clumps; (A)biomass; (v)mean total mycelial length.
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120
Figure 7. Variation in mean total mycelial length and mean lengths of zero-, first-, and second-order branches of mycelia from a 7-L batch S. clauuligerus fermentation. Bars: (solid) mean total mycelial length; (open) mean zero-order length; (hatched) mean first-order length; (criss-crossed) mean secondorder length.
characterizing clumps, and additionally for the 120-h sample using a stripped-down version undertaking only the same tasks as those of Packer and Thomas (1990). Table I1 gives the timings of the full program on samples from the P. chrysogenum fermentation.
Discussion Figure 5 illustrates how this fully automatic image analysis method might be used for characterization of filamentous microorganisms growing in a freely dispersed form in submerged culture. It should be understood that the standard errors of the means (Figure 5) are largely due to the continuing process of hyphal growth and breakage leading to a wide range of mycelial sizes and structures, even within a single sample. Before image analysis was utilized allowinga large sample size to be rapidly measured, this variability made it difficult to collect reliable morphological data especially from batch fermentations (Metz et al., 1981). As shown in Tables I and 11, image analysis has the speed required to analyze enough hyphae from each sample
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Figure 10. Variation in the two fullness ratios throughout the time course of the test P. chrysogenum fermentation. Symbols: (v) Fullness ratio calculated using area within the actual perimeter of clump; ( 0 )Fullness ratio calculated using the area within convex perimeter of clump.
to obtain meaningful results in a reasonably short time. In the case of the S. clauuligerus, both the full program and stripped-down version required 0.16 min or less per microorganism, meaning a typical analysis time for 100 microorganisms of approximately 16 min. It can be seen that the mean time taken per field of view hardly varies with the time of sampling, and the timings are dependent
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Table I. Timings for the Processing of Samples from a Streptomyces clavuligerus Fermentation Using both the Full and Stripped-Down (Short) Versions of the Method fermentationtime, h total time taken, min no. of fields mean no. of dispersed mycelia per field mean time per field, min mean time per hyphae, min
24 17.3 15 6.8
48 15.4 14 7.14
full 72 96 19.5 18.9 21 18 4.95 5.83
short 120 120 16.6 10.9 15 16 6.93 6.75
1.15 1.10 0.93 1.05 1.11 0.68 0.17 0.15 0.18 0.18 0.16 0.1
Table 11. Timings for the Processing of Samples from a 6-LFed Batch Penicillium chrysogenum Fermentation Using the Full Method fermentation time, h 56 82 100 140 total time taken, min 130 170 102 163 144 196 113 196 no. of fields 0.88 0.51 mean no. of dispersed mycelia 0.70 0.51 per field mean time taken field. min 0.91 0.87 0.91 0.83 mean time per mycelia, min 1.29 1.71 1.02 1.63
on the number of free microorganisms found in each field. The P. chrysogenum test fermentation, having a higher percentage of clumped mycelia, thus requires a longer analysis time, as shown in Table 11. Using a digitizing table, and semimanual and fullyautomatic image analysis methods, Packer and Thomas (1990) obtained results similar (for a limited range of measurements) to those in this study, when analyzing the streptomycete fermentation illustrated in Figure 5. This implies that the present method would compare well with manual analysis, for those measurements where the latter is possible. However, the method of Packer and Thomas (1990)did not characterize individual branches, as reported here (Figures 6 and 7). Branch measurements permit further analysis of fragmentation and extension events. Figure 6 suggests how the early fragmentation process between 24 and 48 h, seen in Figure 5, can be interpreted as a loss of first- and second-order branches leading to an increase in the proportion of unbranched mycelia. Figure 7 also suggests that the change in total hyphal length observed between 24 and 48 h was mainly due to loss of branches rather than any widespread breakage of main hyphal filaments (zero-order branches), as there appeared to be only small changes in the branch lengths during the process. Similarly, as there was little change in the mean zero-order length between the samples at 48 and 72 h, the subsequent reextension appears to be primarily a rebranching process resulting in an increased proportion of branched mycelia. Although the individual branch widths were also measured, the resolution of 1.09 pm/pixel was not sufficient to make the values reliable. Mean values for the width of the whole mycelia were typically accurate to *20%. Measurements of morphology in the detail provided by this method will be valuable for physiological studies on filamentous microorganisms, particularly on the effect of fermentation conditions on mycelial extension and branching. As stated by Bazin et al. (1991), it is often necessary to obtain frequency distributions of branch lengths (rather than total hyphal lengths) if the applicability of models concerning mycelial morphogenesis is to be tested. This appears to be the case for the stochastic model of early mycelial growth derived by Kotov and Reshetnikov (1990). However, characterization of individual mycelia is not the only function performed by this method. Although the percentage of material in aggregates can reach as high as 97 % (Figure 8), such material has until now been largely ignored in previous work, except in the extreme case of pellet formation. This program for the first time allows
the detailed characterization of mycelial clumps (Figures 8,9, and lo), the formation of which is important in the determination of a mycelial broth's rheological properties (Deindoerfer and Elmer, 1955; Deindoerfer and West, 1960). It is not yet clear which of the parameters chosen here for the characterization of clumps, especially the two fullness ratios (Figure lo), will prove most important in determining broth rheology. Studies are currently being undertaken to investigate the relationship between broth rheology and mycelial morphology measured using the method described here. Although the method described here was developed on a Quantimet 570 (Leica Cambridge plc.), the image processing routines used are either available on, or could easily be implemented on, other commercial image analyzers (possibly with some loss of speed). Use of the Quantimet 570 in this work has demonstrated that fullyautomated image analysis is a powerful tool for the comprehensive and quantitative analysis of mycelial morphology. It will play an important part in future physiological and rheological studies on filamentous microorganisms.
Acknowledgment
This work was supported by the Scienceand Engineering Research Council, U.K. Streptomyces clavuligerus samples were provided by M. T. Belmar-Beiny. Literature Cited Adams, H. L.; Thomas, C. R. The use of image analysis for morphologicalmeasurements on filamentous microorganisms. Biotechnol. Bioeng. 1988,32,707-712. Allan, E. J.;Prosser, J. I. Mycelialgrowthand branchingof Streptomyces coelicolorA3(2)on solid medium. J.Gen. Microbiol. 1983,129,2029-2036. Atkinson, B.; Daoud, I. S. Microbial flocs and flocculation in fermentation process engineering. Advances in Biochemical Engineering; Springer-Verlag: Berlin, Heidelberg, New York, 1976;Vol. 4. Bazin, M. J.; Katz, A. C.; Trinci, A. P. J. Fragment Length Distribution of Non-branching Filamentous Organisms in Suspension Culture. Binary 1991,3, 113-117. Belmar-Beiny, M. T.; Thomas, C. R. Morphology and clavulanic acid production of Streptomyces clavuligerus: Effect of stirrer speed in batch fermentations. Biotechnol Bioeng. 1991,37, 456-462. Deindoerfer, F. H.; Elmer, L. G., Jr. Effects of liquid physical properties on oxygen transfer in penicillin fermentation. Appl. Microbiol. 1955,3,252-257. Deindoerfer, F. H.; West, M. Rheological properties of fermentation broths. Adu. Appl. Microbiol. 1960,2,265-273. Deo, Y. M.; Gaucher, G. M. Semicontinuous and continuous production of penicillin-G by Penicillium chrysogenum cells. Biotechnol. Bioeng. 1984,26,285-295. Kotov, V.; Reshetnikov, S. V. A stochastic model for early mycelial growth. Mycol. Res. 1990,94,577-586. Metz, B. From pulp to pellet: an engineering study on the morphology of moulds. Ph.D. Thesis, Delft University of Technology, 1976. Metz, B.; de Bruijn, E. W.; van Suijdam, J. C. Method for quantitative representation of the morphology of moulds. Biotechnol. Bioeng. 1981,23,149-162. Packer, H. L.; Thomas, C. R. Morphological measurements on filamentous microorganismsby fully automatic imageanalysis. Biotechnol. Bioeng. 1990,35, 870-881. Reichl, U.; Buschult, T. K.; Gilles, E. D. Study of the early growth and branching of Streptomyces tendae by means of an image processing system. J. Microsc. 1990,158,55-62. van Suijdam, J. C.; Metz, B. Influence of engineering variables upon the morphology of filamentous moulds. Biotechnol. Bioeng. 1981,23,111-148. Accepted April 1, 1992.
