The DOI matches its Crossref record; 99 of the 100 references it cites were found in a registry or an index.
Optimization and scale up of industrial fermentation processes
Abstract
To increase product yields and to ensure consistent product quality, key issues of industrial fermentations, process optimization and scale up are aimed at maintaining optimum and homogenous reaction conditions minimizing microbial stress exposure and enhancing metabolic accuracy. For each individual product, process and facility, suitable strategies have to be elaborated by a comprehensive and detailed process characterization, identification of the most relevant process parameters influencing product yield and quality and their establishment as scale-up parameters to be kept constant as far as possible. Physical variables, which can only be restrictedly kept constant as single parameters, may be combined with other pertinent parameters to appropriate mathematical groups or dimensionless terms. Process characterization is preferably based on real-time or near real-time data collected by in situ and on-line measurements and may be facilitated by supportive approaches and tools like neural network based chemometric data analysis and modelling, clarification of the mixing and stream conditions through computational fluid dynamics and scale-down simulations. However, as fermentation facilities usually are not strictly designed according to scale-up criteria and the process conditions in the culture vessels thus may differ significantly and since any strategy and model can only insufficiently consider and reflect the highly complex interdependence and mutual interaction of fermentation parameters, successful scale up in most cases is not the result of a conclusive and straight-lined experimental strategy, but rather will be the outcome of a separate process development and optimization on each scale. This article gives an overview on the problems typically coming along with fermentation process optimization and scale up, and presents currently applied scale-up strategies while considering future technologies, with emphasis on Escherichia coli as one of the most commonly fermented organisms.
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“In a 1997 study, E. coli was evolved in chemostat cultures in order to select for improved growth on glycerol. The authors reported phenotypic changes in terms of colony morphology accompanied with increased growth rate and biomass yield and decreased acetate formation [50].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [50]
In the citing paper
“Glycerol-limited growth led to stable mutants in a single E. coli population, where one mutant showed increased growth in glycerol-sufficient batch cultures and a second mutant did not. Furthermore, the mutant that showed increased growth in batch culture also showed increased fitness under general nutrient limitation, heat and osmotic stress [50].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [50]
Citations
Cites
Fiber-Optic Chemical Sensors and Biosensors
Wolfbeis, O. S. (2006) · Analytical Chemistry · doi:10.1021/ac060490z · Cited by 1,635
High cell-density culture of Escherichia coli
Lee, S. Y. (1996) · Trends in biotechnology · doi:10.1016/0167-7799(96)80930-9 · Cited by 893
Mogk, A., Tomoyasu, T., Goloubinoff, P., Rüdiger, S., Röder, D., Langen, H., & Bukau, B. (1999) · The EMBO Journal · doi:10.1093/emboj/18.24.6934 · Cited by 596
Physiological responses to mixing in large scale bioreactors
Enfors, S., Jahic, M., Rozkov, A., Xu, B., Hecker, M., Jürgen, B., Krüger, E., Schweder, T., Hamer, G., O'Beirne, D., et al. (2001) · Journal of Biotechnology · doi:10.1016/s0168-1656(00)00365-5 · Cited by 480
The bacterial universal stress protein: function and regulation
Kvint, K., Nachin, L., Diez, A., & Nyström, T. (2003) · Current Opinion in Microbiology · doi:10.1016/s1369-5274(03)00025-0 · Cited by 474
Fermentation and Enzyme Technology
Wiseman, A. (1980) · Biochemical Education · doi:10.1016/0307-4412(80)90096-5 · Cited by 344
Fiber-Optic Chemical Sensors and Biosensors
Wolfbeis, O. S. (2002) · Analytical Chemistry · doi:10.1021/ac020176e · Cited by 325
Fermentation and Enzyme Technology
Cooney, C. L., & Demain, A. L. (1979) · Cited by 286
Plasmid Copy Number and Plasmid Stability
Friehs, K. (2003) · Advances in biochemical engineering, biotechnology · doi:10.1007/b12440 · Cited by 283
Bylund, F., Collet, E., Enfors, S., & Larsson, G. (1998) · Bioprocess Engineering · doi:10.1007/s004490050427 · Cited by 231
Research works printed but not linked: 3
- Hammond SV (1992) NIR analysis of antibiotic fermentation. In: Murray I, Cowe IA (eds) Making light work: advances in near infrared spectroscopy, vol 1. VCH, New York, pp 584–589
- Hammond SV, Brookes IK (1992) Near infrared spectroscopy—a powerful technique for at-line and on-line analysis of fermentations. In: Ladisch MR, Bose A (eds) Harnessing biotechnology for the 21st century: proceedings of the 9th international symposium and exhibition. Am Chem Soc, Washington DC, pp 325–333
- Oniscu C, Galaction AI, Cascaval D, Ungureanu F (2001) Modelling of mixing in stirred bioreactors 1. Mixing time for non-aerated simulated broths. Roum Biotechnol Lett 6:119–129
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Adaptive laboratory evolution—Principles and applications for biotechnology
Dragosits, M., & Mattanovich, D. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-64 · Cited by 827
Tripathi, N. K., & Shrivastava, A. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00420 · Cited by 621
Technologies for Beneficial Microorganisms Inocula Used as Biofertilizers
Malusà, E., Sas‐Paszt, L., & Ciesielska, J. (2012) · The Scientific World JOURNAL · doi:10.1100/2012/491206 · Cited by 516
Industrial production of recombinant therapeutics in Escherichia coli and its recent advancements
Huang, C., Lin, H., & Yang, X. (2012) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-011-1082-9 · Cited by 424
Amino acids production focusing on fermentation technologies – A review
D’Este, M., Alvarado-Morales, M., & Angelidaki, İ. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.001 · Cited by 360
Metadata
- Authors
- Schmidt, F. R.
- Affiliations
- Sanofi (Germany) (DE)
- Corresponding author
- Schmidt, F. R.
- Venue
- Applied Microbiology and Biotechnology
- Published
- 2005-07-07
- Volume
- 68
- Issue
- 4
- Pages
- 425-435
- ISSN
- 0175-7598 (ISSN-L)
- 1432-0614
- OpenAlex
- W2024244058
- Cited by
- 362
- References
- 100
- Citations by year
- 2026: 23 · 2025: 37 · 2024: 29 · 2023: 25 · 2022: 20 · 2021: 18 · 2020: 24 · 2019: 19 · 2018: 26 · 2017: 18 · 2016: 19 · 2015: 12 · 2014: 12 · 2013: 12 · 2012: 17
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Update notices
- erratum (2005-10-01)
- Open access
- closed
- Locations
- Applied Microbiology and Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Fluid Dynamics and Mixing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Viral Infectious Diseases and Gene Expression in Insects
- Keywords
- industrial fermentation, process optimization, scale-up, process characterization, microbial stress, product yield, product quality, in situ monitoring, online measurement, chemometrics, artificial neural networks, computational fluid dynamics, Escherichia coli, dimensionless parameters
- MeSH terms
- Computer Simulation, Facility Design and Construction, Fermentation, Industrial Microbiology, Cell Culture Techniques, Bioreactors
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