ReviewScience-based bioprocess design for filamentous fungi Andreas E. Posch, Christoph Herwig, and Oliver Spadiut Vienna University of Technology, Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna, Austria Industrial bioprocesses are commonly based on empiricism rather than scientific process understanding. In this review, we summarize current strategies for sciencebased bioprocess design and control for filamentous fungi aiming at reducing development times and increasing process economics. We discuss recent developments and trends regarding three crucial aspects throughout the bioprocess life cycle of filamentous fungi, namely (i) strain and inoculum characterization, (ii) morphology, and (iii) rheology, as well as their effects on process performance. Complex interconnections between strain, inoculum, morphology, rheology, and process design are outlined and discussed. Only combining different hard type sensors with soft sensor technology and the development of simplified mechanistic models can enable science-based bioprocess design for filamentous fungi. Industrial application of filamentous fungi Economic, reliable, and controllable bioprocesses for filamentous organisms, in particular filamentous fungi, are of utmost importance for the large scale production of a wide range of value-added products including organic acids, enzymes, and antibiotics. Due to complex interactions between process technology, filamentous morphology (Figure 1), and overall process performance, traditional bioprocess design is still commonly carried out stepwise by time-consuming, empirical strategies (Figure 2). In contrast to biopharmaceuticals, the majority of products from filamentous fungi are industrial (white) biotechnological bulk products and thus not subject to tight regulatory demands. Consequently, manufacturers can continuously optimize their production strains and industrial processes to ensure competitiveness. Common empirical approaches, however, lack scientific insight into process technology and key process parameters (KPPs) (see Glossary) inevitably leading to sub-optimally designed bioprocesses and high process failure rates. Therefore, bioprocess engineers pursue two overall goals summarized as ‘two-times 50%’, which means twice the productivity and reducing bioprocess development time to 50%. In order to meet these economic requirements, it is necessary to understand and control the biological system used. In this review, we give an overview of recently developed measurement and control strategies for major Corresponding author: Spadiut, O. (
[email protected]) Keywords: bioprocess development; bioprocess characterization; filamentous fungi; strain screening; morphology; rheology. obstacles throughout the bioprocess life cycle of filamentous fungi that significantly affect the overall process performance. Points that impact the process performance are (i) strain and inoculum, (ii) morphology, and (iii) rheology. Furthermore, we provide future perspectives targeting a holistic understanding of complex interdependencies in science-based process design for filamentous fungi. Strain and inoculum Screening for strain characteristics The first step in bioprocess design is the identification of promising candidate strains (Figure 2). To achieve automation and high-throughput, strains are commonly identified in micro titer plate (MTP) systems. Besides identification of highly productive strains, initial screening should also consider morphological characteristics, such as complex growth morphology or adherent wall growth, which significantly affect reproducibility of results and the subsequent scale-up procedure. Missing these important factors, selected clones may fail at delivering productivities observed at small scale. Reducing the headspace was proven to be a useful strategy for preventing wall growth and consequent heterogeneities [1–3]. Another study compared physiological and morphological process performance in MTPs and 1-liter benchtop reactors. Addition of glass beads promoted mycelial growth Glossary Carbon dioxide evolution rate and oxygen uptake rate (CER/OUR): respiratory rates reflecting physiological culture activity. Critical quality attribute (CQA): a physical, chemical, biological, or microbiological characteristic that should be within an appropriate limit, range, or distribution to ensure desired product quality. Critical process parameter (CPP): a process parameter whose variability has an impact on a critical quality attribute and therefore should be monitored or controlled to ensure that the process produces the desired quality. Design of Experiments (DoE): a structured, organized method for determining the relationship between factors affecting a process and the output of that process. Key process parameter (KPP): an adjustable parameter (variable) of the process that, when maintained within a narrow range, ensures optimum process performance. A key process parameter does not meaningfully affect critical product quality attributes. Ranges for KPPs are established during process development, and changes to operating ranges will be managed within the quality system. Process analytical technology (PAT): a system for designing, analyzing, and controlling manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality. Quality by Design (QbD): a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management. 0167-7799/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tibtech.2012.10.008 Trends in Biotechnology, January 2013, Vol. 31, No. 1 37 Inial invesgaon of process morphology and rheology based on hydraulic scale-down. growth morphology was different.Review [(Figure_1)TD$IG] Trends in Biotechnology January 2013. Morphological life cycle of filamentous fungi. Strain screening [(Figure_2)TD$IG] Characterization of the inoculum Spore inoculum concentration. Process control at manufacture scale based on KPP ranges idenfied in step 2. Although physiological screening results were similar at miniaturized and lab-scale. Smooth integraon of novel clones into the producon is oen hampered by the lack of mechanisc understanding of up-scaling effects. The correct transfer time point from the seed reactor to the production reactor after successful spore germination is as crucial as the optimal spore inoculum quality. Trichoderma spp. Measurement and control of KPP kinecs at limited throughput in liter scale. probably due to differing shear forces. 1 Pellet morphology Disperse morphology Developing hyphae Spores TRENDS in Biotechnology Figure 1. The development strategy starts with qualitative screening of suitable clones. To date however. 2. A disposable microbioreactor system for high-throughput screening of germination efficiency was introduced recently [5]. High shear forces and nutrient limitation cause pellet disintegration. Vol. Another crucial factor that has to be considered when analyzing strain characteristics is the growth medium (Box 1). for antibiotics. for cellulases. 38 . and productivity [7]. Although such miniaturized reactors do not allow for complete characterization of growth morphology. Implementaon at producon scale and concomitantly decreased wall growth [4]. key process parameters (KPPs) that ensure that the biological system is steered to target product quantity are identified. physiology. 3. mechanisc scale-up models. for the production of enzymes or organic acids. Using this approach. The authors also studied the influence of different cultivation regimes on morphological process behavior by measuring particle size distributions. Consequently. Fungal bioprocesses are commonly inoculated from spores. TRENDS in Biotechnology Figure 2. physiological performance parameters identified during MTP screening were similar to benchtop reactors. extending. or Penicillium spp. After successful germination: branching. 31. Moreover. such as the hyphal growth unit length. followed by quantitative bioprocess development in lab-scale bioreactors. fluorescence microscopy [8] and Fourier transform infrared (FTIR) spectromicroscopy [9. Thereby. Traditional bioprocess design for filamentous fungi. High-throughput screening for highly producing strains. Screening of process morphology and rheology at this stage demands verified.4.10] provide spatial resolution of the intracellular biochemical composition of filamentous fungi. and scale-up effects [53]. In this respect. it was shown that morphological parameters. initial processes performed with a fresh inoculum batch only gradually reach an empiric optimum after several test runs. Independent of capitalizing Aspergillus spp. quality. Process development 1. Over process duration. This procedure completely disagrees with the overall ‘two-times 50%’ target in industrial (white) biotechnology. analyzing adaptation of the process parameters. pellets may develop via growth from single spores or by agglomeration. No. results show promise for a means to quantify spore inoculum quality in the future. these filamentous industrial workhorses all share a unique and complex growth morphology. and entanglement of single hyphae introduce disperse hyphal networks. could be correlated to pellet formation kinetics and pellet size [6].5] represent useful tools for the characterization of germination efficiency and spore viability. parallel microbioreactor devices and MTP cultivation systems [1. Reactor chambers of 100 ml volume enabled parallelized characterization of growth morphology during spore germination as well as quantification of inoculum quality. Subsequent piloting aims at scalability of the process. Although these methodologies are still in their infancy. the amount of inoculum and the setting of process parameters are still empirically optimized with each inoculum batch. and viability severely affect fungal morphology. In fact. knowledge of inoculum and its coagulation characteristics should be integrated into a global morphological population balance as well as predictive multivariate models [13].22. However. Major drawbacks of spectroscopic tools are sensor fouling during inline application. multi-wavelength fluorescence spectroscopy. In order to reliably describe the behavior of filamentous fungi over the production process. For the characterization of complex media components. Other studies have investigated morphological changes during prolonged cultivation periods [21]. Morphology significantly influences several other KPPs including broth viscosity. This methodology is based on minimal analytical needs and the indirect determination of specific uptake rates of complex media components by applying statistically verified redundant mass balancing techniques.27]. For strains of non-coagulating fungi. there are two strategies to tackle this problem: (i) characterizing complex raw materials and taking corresponding actions depending on the variations. Measurement of the carbon dioxide evolution rate (CER).56]. mid infrared (MIR). Hence. mechanical forces. Screening for relevant physiological and morphological characteristics at this initial stage of bioprocess design will facilitate prediction of large scale process behavior. spectroscopic techniques. The spore inoculum concentration is a KPP for pellet formation. as well as the inoculum concentration and viability [7.13]. reliable morphology analysis and control are crucial for optimized bioprocesses. on fungal morphology [14. containing valuable suggestions for possible applications of the respective technique. can only be gathered if accurate tools for morphological analysis are available. and substrate diffusion. Several studies have analyzed the event of conidial aggregation.20]. and near-infrared (NIR) spectroscopy are used to determine this time point online [11. including spore inoculum. avoiding raw material variability by switching to defined cultivation media is preferable. and attenuated total reflectance mid infrared (ATR-MIR) spectroscopy are commonly employed [54].15. a description of the phenomena of conidial aggregation. the review showcases existing approaches to elucidate relevant. (ii) at germination. Certain growth morphologies are directly related to the production of industrially relevant compounds. Morphology Coagulating versus non-coagulating species The mechanism of pellet formation depends on the fungal strain. No.12]. only affected by the pH value and the osmolality of the medium [14]. Hence. but also strain-specific parameters like productivity and yields [22].26. Nutrient limitation and oxygen diffusion gradients affecting pellet micromorphology [16] as well as effects of dO2 and dCO2 concentrations in the bioreactor on fungal macromorphology have all been quantified recently [17– 19]. and agitation [15]. This approach has not only enabled the switch from complex to defined media but also a twofold increase of the overall penicillin space time yield and a threefold increase in the maximum specific penicillin formation rate [3]. recently published review discusses methods to analyze fungal morphology including atomic force microscopy. Media development To date. and (ii) switching production processes from complex to defined media. 31. Although for some of these techniques applicability in the production environment still needs to be proven. pellet formation accelerates at reduced spore inoculum concentrations [15]. The predominant morphology during cultivations depends on several factors like the strain. mixing power demands. whereas mycelial growth enhances formation of fructofuranosidase [14]. So 39 . Aggregation occurs in two steps: (i) immediately after inoculation. Reliable analytical techniques should allow detailed investigation of the effects of changes in process parameters. Such knowledge. for the sake of simplicity and controllability. An excellent. This is especially important for process scalability because hydrostatic backpressures at the bottom of large vessels may create high residual dCO2 concentrations [17.18. the spore inoculum concentration and quality. which is further influenced by the media. during which substrate concentration gradients across fungal pellets affect pellet micromorphology and biomass activity [16]. osmolality. Fast strain characterization of filamentous fungi in batch cultivations on combined complex/defined media allows identification of the most promising candidate strain for switching the bioprocess from complex to fully defined media [3]. pellets either form from a single spore that germinates or from entanglements Trends in Biotechnology January 2013. agitation. Currently. Conidial aggregation can be nicely monitored by 2D fluorescence spectroscopy [11] and has been described in a reliable process model [7]. affected by pH.25].22. turbidity measurement. and real-time applicable techniques [25]. Morphology and rheology interdependencies can be investigated by digital image analysis [23] or particle size analysis [24].Review Box 1. Spectroscopic approaches do not require off-gas analysis and thus also apply to MTPs and shake flasks. significant lot-to-lot variability of different components in complex media results in large variations in the production efficiency. A fungal strain can be coagulating or non-coagulating. most of the established production processes with filamentous fungi are performed in chemically undefined media. extensive calibration procedures for reliable quantitative chemometric models as well as the demand for complex multivariate data analysis [54. media composition. A comprehensive overview of most of these methods. however. flow cytometry. pellets are preferred for citric acid production [15]. 1 of branched hyphae and disperse mycelial clumps. Vol. and aeration [13]. The major advantages of complex media are that they contain a rich variety of compounds allowing unimpaired growth of microorganisms and that they are made from inexpensive sources. Miniaturized and parallel screening approaches provide fast quantification of strain and inoculum characteristics. for example. A prerequisite for morphological process control is the detailed understanding of a fungus’ morphological response to process parameters. was given in 2004 [55]. Morphology analysis and control Growth morphology is increasingly targeted as a KPP during process development because of the undisputable link between fungal morphology and process performance [15. mechanistic morphological information for optimized bioprocess development. and mixing kinetics. Different morphologies during production processes Most filamentous fungi processes are multi-stage cultivations during which filamentous fungi exhibit different morphologies. including near infrared (NIR). and breakage can be quantified by confocal laser scanning microscopy [16]. for example. giving access to mechanistic morphological information. No. Hydrodynamic conditions in the bioreactor influence the morphological development of the fungus and thus the rheology. hydrodynamic conditions affect pellet formation on three levels: (i) interaction of pellets and eddies. Unless there is a direct linkage between morphology and productivity. Posch et al. it is crucial to know the energy dissipation by impellers and aeration. Moreover.and thus bioprocess-control in the future. Updating mechanistic. the lack of reliable tools for automated high-throughput analysis of fungal morphology has prevented the usage of morphological analysis as a routine analysis procedure. The average energy input. High viscosity causes insufficient mass transfer and oxygen limitation [41]. and rheology [23. and redundant elemental mass balancing for estimation of biomass production [3] may be a promising path to facilitate realtime monitoring of biomass characteristics by soft sensors. physiology. however.E. Fractal dimensions measure the degree of complexity as well as mass filling properties. Here. and productivity has to be integrated into models. Fractal dimensions can be employed as morphological descriptors [28] and also describe branching behavior and pellet morphology [29]. Similarly.32]. these analyses have mostly been based on a limited number of manually recorded microscopic images using digital image analysis techniques introduced more than a decade ago [27]. Higher energy dissipation causes strong erosion and changes in pellet morphology as shown for A. mass transfer. We believe that high-throughput morphological characterization approaches ranging from light microscopy to flow cytometry [6. erosion. In general. can be used to characterize fluid dynamics and rheology. novel tools for micromorphological analysis include (i) FTIR spectromicroscopic imaging. To date.35]. pellet morphology is preferred in production processes due to lower viscosity. another morphological descriptor. For closer insights.40]. and (iii) collision among pellets [42]. rheology. However. To date. Vol. lead to high shear stress. is a high-throughput. an easily accessible parameter. A recent high-throughput method for fast morphological characterization of filamentous fungi interlinks fully automated recording of thousands of microscopic images with concomitant statistical verification for complete. Freely dispersed fungal growth causes high broth viscosity and impairs oxygen transfer. can be used. intermediate-level morphological characterization approaches. biomass concentration. These interactions may strip off hyphae or completely rupture whole pellets. To reliably describe a fungal bioprocess. Recent. and (ii) MALDI-TOF intact cell mass spectrometry (ICMS). Sound combination of sensors for biomass volume [39]. but still have not been implemented for industrial process development [31. and micromorphology. is closely correlated with productivity for Aspergillus niger [14]. the morphology number. The optimal strategy. Moreover. structured morphological modeling strategies for predicting morphological process behavior and the link to productivity were already introduced in the 90s. 1 process parameters. (ii) impact between pellets and stirrers/baffles.Review far. Such highlevel techniques represent an important step towards mechanistic modeling of interdependencies between 40 Trends in Biotechnology January 2013. Devices for online. Rheology Rheology and morphology There are complex interconnections between morphology. which provides micromorphological analysis that facilitates identification and quantification of protein patterns in different physiological or morphological process states (A. niger [41]. Promising. which allows 3D spatial resolution of pellet micromorphology by investigating the biochemical composition of pellet microtome sections. and real-time microscopic measurements have been developed in recent years. This event has been spatially resolved by confocal laser scanning microscopy [16]. structured morphological models with quantitative morphological data will advance approaches for predictive 3D morphological modeling of pellet and disperse morphology [38]. Pellet micromorphology and substrate diffusion limitations affecting pellet growth. which are required for optimum mass transfer. In this respect. depends on the experimental set-up and thus . and agitation regime [15]. unpublished). however.24]. and a large number of morphological parameters can be effectively replaced by these indices [28]. 31.24] will facilitate morphology.36]. which causes lysis of pellet core cells [6]. morphological control strategies for industrial production include the selection of a candidate strain that exhibits the desired morphology during process development [6] as well as adjustment of spore inoculum concentration. increasing the amount of free filaments and hence the viscosity. inline. control of osmolality facilitates control of disperse mycelial morphology [14]. microparticles can be used to customize fungal morphology [37]. Modeling fungal fragmentation requires calculation of the hyphal strength and the mechanical forces exerted on the fungus by the turbulent flow. but their applicability as automated online measurement tools is yet to be demonstrated [33]. we highlight the dependency of the rheology on fungal morphology. however. dielectric (DE) spectroscopy. Because manual image recording prevents high-throughput applicability. media composition. Consequently. the influence of hydrodynamic shear stress on morphology. accurate quantification of fungal morphology [6]. mycelial networks are fragmented. High agitation rates. automatable lowlevel morphological characterization approaches based on particle size distributions should be used for industrial routine applications [4. Despite the availability of computational workflows minimizing manual intervention during morphological analyses [34.. high-level morphological characterization approach describing fungal morphology based on morphological classification and frequency distributions for several morphological parameters [6]. there is still a need for image analysis systems with fully automated image acquisition and analysis [14. This parameter. pellet growth limits substrate diffusion to the pellet core at critical pellet diameters. Such approaches allow modeling and understanding of heterogeneities that affect the overall process performance based on population balances [30]. Over the coming years. Shear forces are unequally distributed within the reactor. Predicting rheology Projected power requirements from lab to large scale often diminish process economics or even exceed power availability. oxygen saturation. the Metzner and Otto method power law. Current approaches combine particle size distribution data and biomass concentration and analyze these data using different chemometric tools. volumetric power input. Important formulas to describe different aspects influencing the rheology are the well-known formulas for the apparent viscosity. by media adaptation and introduction of dilution steps [20]. for example. and the Herschel-bulkley model. as well as their concomitant improvement. (ii) rotational techniques. cannot be transferred across models. and high-throughput accessible size distribution data can be used to predict rheological properties in a bioreactor [24]. have been achieved [43. 1 Box 2. Rheology measurement tools Reliable. complementing measurement techniques by sound soft sensors already enables resolving the interplay of highly cross-linked KPPs and thus describes a promising approach towards science-based bioprocess design for filamentous fungi. Recent developments in instrument technology for rheometry. A holistic model has to use transferable parameters and describe all processes independent of the operating conditions. so the maximum shear rate rather than average parameters should be used to describe the rheology in a bioreactor [41]. bioprocesses with filamentous fungi need to be adapted during piloting to meet available power at manufacturing scale.38] with highdimensional quantitative morphological population data [6]. Fungal fermentation broths are a complex system: at the beginning of a process the broth basically describes a Newtonian liquid. Combination of orthogonal. it is still very complex and difficult to predict the rheology of a fermentation broth. Thus. Traditional techniques can be grouped into three categories: (i) capillary techniques. kla value. 31. biomass concentration. a single formula cannot describe the complex processes contributing to the viscosity in a bioreactor. we will see the implementation of recently introduced advanced sensors for strain and inoculum characterization. and (iii) falling ball techniques [43]. We believe that superior particle characterization methods (highthroughput. Science-based process design applying morphological rheological modeling for predictive morphological scale-up would represent a major step towards increasing the process success rate and thus contribute at reducing process development times and increasing process economics (Figure I). Hence. but due to biomass growth and changing fungal morphology. the calibrated model is specific for a certain fungal strain. However. especially with regard to nonNewtonian liquids. The most important parameters to describe broth rheology can be successfully predicted from size distribution. and process performance. Extraction of relevant information from such accessible multidimensional data by multivariate analysis and integration into simplified. electromagnetic acoustic resonators. and mixing time [57–59]. This will allow more accurate recording of KPPs and give access to a much greater number of (online) process data. we suggest predictive morphological scale-up along with scalable. mechanistic process models will be necessary to increase process success rate from miniaturized screening to manufacturing scale. miniaturized devices. [(Box_2)TD$FIG] multivariate methods. However. morphology. the scale and the operating conditions and thus only valid in a limited range. rheology. nonlinear control strategies for reliable extrapolation of production process performance from screening results. including ultrasound spectroscopic tools. such as partial least squares (PLS) models. scale-up of filamentous bioprocesses is commonly targeted using empirical rule of thumbs for hydraulic scale-up including the maximum shear rate or impeller tip speed. Complementing conventional low-throughput image analysis for rheological modeling [23]. Thus. Recommended strategies for science-based process design targeting reduced process development times and increased process economics. However. rheology is commonly not measured in real-time for filamentous bioprocesses. these techniques cannot be used for fungal fermentation broths because they are considered to be non-Newtonian liquids. automatable approaches for timely determination of rheological broth characteristics are still needed. Vol. statistically verified image analysis) will significantly increase the predictive power of rheology models in the future by providing mechanistic modeling approaches [23. These multivariate approaches model rheological properties of filamentous fermentation broths with equal or greater accuracy than traditional power-law type relationships based on population average data from image analysis [24]. Unfortunately. the interaction between eddies and the pellet surfaces needs to be related to shear forces. and vibrating bridge devices are still in their infancy but describe a promising online tool for 41 . the broth becomes heterogeneous exhibiting non-Newtonian characteristics.44]. To date. No.Review Trends in Biotechnology January 2013. and feed mode. Science-based process design Rheology Miniaturizaon and parallelizaon High-throughput analysis and data verificaon Online monitoring and modeling Morphology Strain and inoculum TRENDS in Biotechnology Figure I. Novel. Despite the availability of inline viscometers from the chemical industry. 50].47] [12. and the biomass viability [39]. such as the rheology [23]. the physiological process state should be reliably monitored (Box 3). the biomass concentration.46. 47] [11. not applicable. which are closely related to biomass growth [51]. throughout all stages of bioprocess design. The metabolic heat production correlates with growth. IR spectroscopy. with hard type sensors. shows cross-sensitivities. like the carbon dioxide evolution rate (CER). the target parameter in industrial process design. In summary. measuring filamentous broth viscosity. 12.50] [51] – – – – – R A A A [51] – – – – – – NA A A Soft sensors – – – – – – A A A [3.. Furthermore. the oxygen uptake rate (OUR).Review Trends in Biotechnology January 2013. Biomass viability. To monitor biomass characteristics. concentration. actively producing cells.39] [3. A. 1 Table 1. In addition to such representative biomass measurements.47] [46. like turbidity measurements. where the respective technology can be used. –. soft sensors are a powerful approach for tracking KPPs. which would only be accessible for direct measurement by costly equipment and manpower. changing yields. 2D spectroscopy. and energy input [45].46. and physiology 42 [49. viability as well as its physiological state should be monitored.50]. morphology. R. Methods for real-time determination of filamentous biomassa Method Cross-sensitivity Gas bubbles – Microscopy R Turbidimetry – Flow cytometry Vibrational spectroscopy R Applicability Refs Solid media components + R R R Salt content – – – – High cell density – R R – Morphology Sensor fouling + R R R + – R/+ R Screening Development Production A A NA A A A A A R R A R Dielectric spectroscopy R R R – + – NA A A Fluorescence spectroscopy Calorimetry Mass balancing from pH/base addition Mass balancing from pO2/OUR from pO2 Off-gas measurement R R – – R/+ R A A R – – – – – – – – + – – R NA A A A A A [33. a large variety of different measurement tools to determine the fungal biomass concentration is available. are influenced by the biomass concentration. quantifiable cross-sensitivity. Currently. case-specific risk assessment necessary due to non-quantifiable cross-sensitivity. during later stages of the process. first principle stoichiometric mass balancing allows online estimation of biomass concentration even for cultivations on complex. which is why this online strategy could be a useful tool for noninvasive morphology monitoring [49. and CER gave the most accurate estimates. Hence. current efforts are focused on reliable models that predict broth rheology based on measurements of more easily accessible parameters including biomass concentration.47] [49. solid media [3]. Biocalorimetry is used to monitor heat production during bioprocesses online.51] a Abbreviations: +. dielectric (DE) spectroscopy. 31. heat release patterns also reflect changes in fungal morphology.46. Vol. the combination of complementary measurement principles with first principle stoichiometric mass balancing in soft sensors is highly recommended. Also KPPs. Effects on process performance To quantify process performance of filamentous fungi. Investigating heat evolution over process time by so-called power-time curves provides information on biomass growth and physiology.11. The viscosity is proportional to the biomass concentration up to the end of the exponential growth phase [48].39.47]. . turbidity measurement. A comparison of common online biomass sensors has shown that DE spectroscopy. particle size distribution [24]. and activity can be estimated to ultimately facilitate predictive process modeling and control. and describes the stages of the fungal bioprocess life cycle. morphology. strongly depends on the biomass concentration and the amount of viable. Accurate measurement of broth rheology as well as its prediction during scale-up is crucial for successful bioprocess design (Box 2). A combination of sensors also reveals information on the morphology of the fungus. applicable. different strategies. Provided that the relevant components have been identified and can be measured online. no cross-sensitivity. Reliable online determination of the biomass concentration and the amount of active biomass would represent a major step towards effective bioprocess control. NA.47] [24. or the base consumption. 46. Biomass concentration and viability Volumetric productivity. Soft sensors can be used to accurately estimate biomass by combining easy-to-monitor data. Broth viscosity can be used as an indicator for the biomass concentration of the filamentous model organism Streptomyces spp. Table 1 gives an overview of these technologies. 54] [39. No. Hence. and different soft sensors have been developed [46. However. viscosity does not indicate biomass concentration due to changes in the rheological properties of the fermentation broth arising from changes in morphology [48]. Implementation of such an automated workflow will also enable fast integration of novel. and process design with physiology and productivity are complex. (2008) In situ monitoring of the seed stage of a fermentation process using non-invasive NIR spectrometry. To date. PAT tools that have been developed for unicellular systems may not be transferred easily to filamentous fungi due to several fungal specific characteristics. Unfortunately. et al. 72. Adv. et al. Eng. Fluoresc. Fungal Genet. (2008) High spatial resolution analysis of fungal cell biochemistry–bridging the analytical gap using synchrotron FTIR spectromicroscopy. (2010) Applicability of Penicillium chrysogenum rheological correlations to broths of other fungal strains.B. 132. 88 4 Sohoni. Lett. Bioeng. it is difficult to develop reliable models for spectroscopic methods. et al. Relevant process information should be extracted from experimental raw data by statistically verified macroscopic balancing and morphological analysis [3. T. However. T. J. http://www. 1779–1789 11 Ganzlin. L. (2008) Effects of dissolved carbon dioxide on growth. 660–666 13 Grimm. et al. and peaks in the spectra can overlap. penicillin synthesis and morphology in batch cultures of Penicillium chrysogenum. et al. and approaches to strain improvement of filamentous fungi. oryzae has been based on common hydraulic parameters including agitation and aeration regimes but also 1st principle relationships for reaction stoichiometry. No. L. 22. et al. Bioeng. N. 109. Cell Fact. Bioeng. broth viscosity. small-scale cultivation platform for Streptomyces coelicolor. G. we believe that following Quality by Design (QbD) guidelines [ICH (2009) Q8.52]. (2006) Dissolved carbon dioxide effects on growth. metabolic activity and quantitative protein production in recombinant Aspergillus niger fed-batch cultures. 614–623 17 El-Sabbagh. (2011) Disposable parallel poly(dimethylsiloxane) microbioreactor with integrated readout grid for germination screening of Aspergillus ochraceus. niger strain during fed-batch cultivations [11]. 443–458 21 Haack. 39. (2007) A synchrotron FTIR microspectroscopy investigation of fungal hyphae grown under optimal and stressed conditions. filtrate samples have to be analyzed separately in order to formulate a model for a certain key analyte [54. Appl. and KPPs followed by systematic resolution of their interdependencies has been demonstrated for the production of biopharmaceuticals [CMC-Biotech-Working-Group (2009) A-Mab: a Case Study in Bioprocess Development. 11. 879–888 14 Wucherpfennig. Biol. decrease development Trends in Biotechnology January 2013. S. Lett. 92. M.g.org] would be also beneficial in the case of industrial (white) biotechnological bulk products. Concluding remarks and future trends Challenges in bioprocess design for filamentous fungi are heavily interdependent (see Figure I in Box 2).Review Box 3. Biomicrofluidics 5. as well as the specific growth rate at varying cultivation regimes. Vol. et al.V. et al.60]. (2004) Kinetic studies on the aggregation of Aspergillus niger conidia. 9 5 Demming. 49. Biotechnol.R. (2007) In situ multi-wavelength fluorescence spectroscopy as effective tool to simultaneously monitor spore germination. (2005) Influence of mechanical stress and surface interaction on the aggregation of Aspergillus niger conidia. (2011) Morphology engineering–osmolality and its effect on Aspergillus niger morphology and productivity. see http://www. 315–324 19 Li. Biomass and broth absorbance may hide absorbance of other analytes. 2D fluorescence spectroscopy can be employed to monitor the productivity and the metabolic activity of a recombinant A. Biotechnol. et al. rheology. Cell Fact. S. M. Bioanal. FEMS Microbiol. such as the complex growth morphology or adherent wall growth affecting probe fouling. Enzyme Microb. inoculum. In Manual of Industrial Microbiology and Biotechnology. A. Technol.casss. 482–487 22 Wucherpfennig. underlying physiological information should be examined with mechanistic modeling [45. Performance of a production process on complex media for a recombinant animal cell line has been nicely predicted by combining NIR and 2D fluorescence spectroscopy [61]. Monitoring physiological process performance Different strategies can be applied and combined for in situ monitoring of the physiological process state. 78. and Thomas. et al. and oxygen transfer. org/associations/9165/files/A-Mab_Case_Study_Version_ 2-1. 185–190 18 El-Sabbagh. 230–234 2 Meyer. Appl. mass transfer.6]. 89–136 23 Riley. and increase process economics for the production of value-added products from filamentous fungi. nutrient consumption. cephalosporin C synthesis and morphology of Acremonium chrysogenum in batch cultures. Microb. et al. (3rd edn). In this context. et al. 189–259 16 Hille. et al. et al. We believe that this strategy also describes a potential tool for bioprocesses with filamentous fungi. Glucose in Streptomyces coelicolor cultivations has been accurately monitored by NIR spectroscopy [60]. 1 times. J. et al. (2009) Pilot-scale process development and scale up for antifungal production. Bioprocess Biosyst. (2012) Robust. 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