Abstract
During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
No datasets were generated or analysed during the current study.
References
Abiodun OI, Jantan A, Omolara AE et al (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4:e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Aehle M, Bork K, Schaepe S et al (2012) Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach. Cytotechnology 64:623–634
Aehle M, Kuprijanov A, Schaepe S et al (2011a) Increasing batch-to-batch reproducibility of CHO cultures by robust open-loop control. Cytotechnology 63:41–47. https://doi.org/10.1007/s10616-010-9320-y
Aehle M, Schaepe S, Kuprijanov A et al (2011b) Simple and efficient control of CHO cell cultures. J Biotechnol 153:56–61. https://doi.org/10.1016/j.jbiotec.2011.03.006
Allampalli P, Rathinavelu S, Mohan N, Sivaprakasam S (2022) Deployment of metabolic heat rate based soft sensor for estimation and control of specific growth rate in glycoengineered Pichia pastoris for human interferon alpha 2b production. J Biotechnol 359:194–206. https://doi.org/10.1016/j.jbiotec.2022.10.006
Beiroti A, Hosseini SN, Aghasadeghi MR, Norouzian D (2019) Comparative study of μ -stat methanol feeding control in fed-batch fermentation of Pichia pastoris producing HBsAg: an open-loop control versus recurrent artificial neural network-based feedback control. J Chem Technol Biotechnol 94:3924–3931. https://doi.org/10.1002/jctb.6192
Biener R, Steinkämper A, Hofmann J (2010) Calorimetric control for high cell density cultivation of a recombinant Escherichia coli strain. J Biotechnol 146:45–53. https://doi.org/10.1016/j.jbiotec.2010.01.004
Biener R, Steinkämper A, Horn T (2012) Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae. J Biotechnol 160:195–201. https://doi.org/10.1016/j.jbiotec.2012.03.006
Butkus M, Repšytė J, Galvanauskas V (2020) Fuzzy logic-based adaptive control of specific growth rate in fed-batch biotechnological processes. A simulation study. Appl Sci 10:6818. https://doi.org/10.3390/app10196818
Cheng Y, Bi X, Xu Y et al (2023) Artificial intelligence technologies in bioprocess: opportunities and challenges. Biores Technol 369:128451. https://doi.org/10.1016/j.biortech.2022.128451
Chenikher S, Guez JS, Coutte F et al (2010) Control of the specific growth rate of Bacillus subtilis for the production of biosurfactant lipopeptides in bioreactors with foam overflow. Process Biochem 45:1800–1807. https://doi.org/10.1016/j.procbio.2010.06.001
Dabros M, Schuler MM, Marison IW (2010) Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass. Bioprocess Biosyst Eng 33:1109–1118. https://doi.org/10.1007/s00449-010-0438-2
De Battista H, Picó J, Picó-Marco E (2012) Nonlinear PI control of fed-batch processes for growth rate regulation. J Process Control 22:789–797. https://doi.org/10.1016/j.jprocont.2012.02.011
De Battista H, Picó J, Picó-Marco E, Mazzone V (2007) Adaptive sliding mode control of fed-batch processes using specific growth rate estimation feedback. IFAC Proc Vol 40:127–132. https://doi.org/10.3182/20070604-3-MX-2914.00023
Duan S, Shi Z, Feng H et al (2006) An on-line adaptive control based on DO/pH measurements and ANN pattern recognition model for fed-batch cultivation. Biochem Eng J 30:88–96. https://doi.org/10.1016/j.bej.2006.02.007
Ehgartner D, Hartmann T, Heinzl S et al (2017) Controlling the specific growth rate via biomass trend regulation in filamentous fungi bioprocesses. Chem Eng Sci 172:32–41. https://doi.org/10.1016/j.ces.2017.06.020
Escalante-Sánchez A, Barrera-Cortés J, Poggi-Varaldo HM et al (2018) A soft sensor based on online biomass measurements for the glucose estimation and control of fed-batch cultures of Bacillus thuringiensis. Bioprocess Biosyst Eng 41:1471–1484. https://doi.org/10.1007/s00449-018-1975-3
Fonseca RR, Franco IC, Da Silva FV. Bioreactor temperature control using a generic fuzzy feedforward control system. In: 15th IASTED international conference intelligent systems and control (ISC 2016)
Fonseca RR, Sencio RR, Franco IC, Da Silva FV (2018) An adaptive fuzzy feedforward-feedback control system applied to a saccharification process. Chem Prod Process Model. https://doi.org/10.1515/cppm-2018-0014
Forbes MG, Patwardhan RS, Hamadah H, Gopaluni RB (2015) Model predictive control in industry: challenges and opportunities. IFAC-PapersOnLine 48:531–538. https://doi.org/10.1016/j.ifacol.2015.09.022
Galvanauskas V, Simutis R, Levišauskas D, Urniežius R (2019a) Practical solutions for specific growth rate control systems in industrial bioreactors. Processes 7:693. https://doi.org/10.3390/pr7100693
Galvanauskas V, Simutis R, Vaitkus V (2019b) Adaptive control of biomass specific growth rate in fed-batch biotechnological processes. A comparative study. Processes 7:810. https://doi.org/10.3390/pr7110810
Gautam A, Sahai V, Mishra S (2021) Development of a dual specific growth rate-based fed-batch process for production of recombinant human granulocyte colony-stimulating factor in Pichia pastoris. Bioprocess Biosyst Eng 44:103–112. https://doi.org/10.1007/s00449-020-02427-0
Glassey J (2013) Multivariate data analysis for advancing the interpretation of bioprocess measurement and monitoring data: measurement, monitoring, modelling and control of bioprocesses. Adv Biochem Eng Biotechnol 132:167–191
Gnoth S, Jenzsch M, Simutis R, Lübbert A (2008) Control of cultivation processes for recombinant protein production: a review. Bioprocess Biosyst Eng 31:21–39. https://doi.org/10.1007/s00449-007-0163-7
Haack MB, Lantz AE, Mortensen PP, Olsson L (2007) Chemometric analysis of in-line multi-wavelength fluorescence measurements obtained during cultivations with a lipase producing Aspergillus oryzae strain. Biotechnol Bioeng 96:904–913. https://doi.org/10.1002/bit.21170
Habegger L, Rodrigues Crespo K, Dabros M (2018) Preventing overflow metabolism in crabtree-positive microorganisms through on-line monitoring and control of fed-batch fermentations. Fermentation 4:79. https://doi.org/10.3390/fermentation4030079
Henes B, Sonnleitner B (2007) Controlled fed-batch by tracking the maximal culture capacity. J Biotechnol 132:118–126. https://doi.org/10.1016/j.jbiotec.2007.04.021
Hisbullah MH, Ramachandran K (2002) Comparative evaluation of various control schemes for fed-batch fermentation. Bioprocess Biosyst Eng 24:309–318. https://doi.org/10.1007/s00449-001-0272-7
Hu R, Cui R, Xu Q et al (2022) Controlling specific growth rate for recombinant protein production by Pichia pastoris under oxidation stress in fed-batch fermentation. Appl Biochem Biotechnol 194:6179–6193. https://doi.org/10.1007/s12010-022-04022-3
Ibáñez F, Saa PA, Bárzaga L et al (2021) Robust control of fed-batch high-cell density cultures: a simulation-based assessment. Comput Chem Eng 155:107545. https://doi.org/10.1016/j.compchemeng.2021.107545
Jacobs PP, Inan M, Festjens N et al (2010) Fed-batch fermentation of GM-CSF-producing glycoengineered Pichia pastoris under controlled specific growth rate. Microb Cell Fact 9:93. https://doi.org/10.1186/1475-2859-9-93
Jae-Ho L, Choi Y-H, Kang S-K et al (1989) Production of human leukocyte interferon in Escherichia coli by control of growth rate in fed-batch fermentation. Biotech Lett 11:695–698
Jenzsch M, Gnoth S, Beck M et al (2006a) Open-loop control of the biomass concentration within the growth phase of recombinant protein production processes. J Biotechnol 127:84–94. https://doi.org/10.1016/j.jbiotec.2006.06.004
Jenzsch M, Gnoth S, Kleinschmidt M et al (2006b) Improving the batch-to-batch reproducibility in microbial cultures during recombinant protein production by guiding the process along a predefined total biomass profile. Bioprocess Biosyst Eng 29:315–321. https://doi.org/10.1007/s00449-006-0080-1
Jenzsch M, Simutis R, Luebbert A (2006c) Generic model control of the specific growth rate in recombinant Escherichia coli cultivations. J Biotechnol 122:483–493
Jia L, Rao S, Li H et al (2022) Enhancing HSA-GCSFm fusion protein production by Pichia pastoris with an on-line model-based exponential and DO-stat control modes. Biochem Eng J 177:108262. https://doi.org/10.1016/j.bej.2021.108262
Johnsson O, Andersson J, Lidén G et al (2013) Feed rate control in fed-batch fermentations based on frequency content analysis. Biotechnol Prog 29:817–824. https://doi.org/10.1002/btpr.1727
Justice C, Brix A, Freimark D et al (2011) Process control in cell culture technology using dielectric spectroscopy. Biotechnol Adv 29:391–401. https://doi.org/10.1016/j.biotechadv.2011.03.002
Kager J, Tuveri A, Ulonska S et al (2020) Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process. Process Biochem 90:1–11. https://doi.org/10.1016/j.procbio.2019.11.023
Katla S, Mohan N, Pavan SS et al (2019) Control of specific growth rate for the enhanced production of human interferon α2b in glycoengineered Pichia pastoris : process analytical technology guided approach. J of Chemical Tech Biotech 94:3111–3123. https://doi.org/10.1002/jctb.6118
Kottelat J, Freeland B, Dabros M (2021) Novel strategy for the calorimetry-based control of fed-batch cultivations of Saccharomyces cerevisiae. Processes 9:723. https://doi.org/10.3390/pr9040723
Kuprijanov A, Schaepe S, Simutis R, Lübbert A (2013) Model predictive control made accessible to professional automation systems in fermentation technology. Biosyst Inf Technol 2:26–31
Landau ID, Lozano R, M’Saad M, Karimi A (2011) Adaptive control: algorithms, analysis and applications. Springer London, London
Landau RN (1996) Expanding the role of reaction calorimetry. Thermochim Acta 289:101–126. https://doi.org/10.1016/S0040-6031(96)03081-X
Larsson C, Lidn G, Niklasson C, Gustafsson L (1991) Calorimetric control of fed-batch cultures of Saccharomyces cerevisiae. Bioprocess Eng 7:151–155. https://doi.org/10.1007/BF00387410
Lee J, Lee SY, Park S, Middelberg APJ (1999) Control of fed-batch fermentations. Biotechnol Adv 17:29–48. https://doi.org/10.1016/S0734-9750(98)00015-9
Levisauskas D (2001) Inferential control of the specific growth rate in fed-batch cultivation processes. Biotech Lett 23:1189–1195. https://doi.org/10.1023/A:1010528915228
Li M, Ebel B, Blanchard F et al (2019) Control of IgG glycosylation by in situ and real-time estimation of specific growth rate of CHO cells cultured in bioreactor. Biotechnol Bioeng 116:985–993. https://doi.org/10.1002/bit.26914
Liu W, Xiang H, Zhang T et al (2020) Development of a new high-cell density fermentation strategy for enhanced production of a fungus β-glucosidase in Pichia pastoris. Front Microbiol 11:1988. https://doi.org/10.3389/fmicb.2020.01988
Mahmoodi M, Nassireslami E (2022) Control algorithms and strategies of feeding for fed-batch fermentation of Escherichia coli : a review of 40 years of experience. Prep Biochem Biotechnol 52:823–834. https://doi.org/10.1080/10826068.2021.1998112
Mandenius C-F (2004) Recent developments in the monitoring, modeling and control of biological production systems. Bioprocess Biosyst Eng 26:347–351
Maskow T, Harms H (2006) Real time insights into bioprocesses using calorimetry: state of the art and potential. Eng Life Sci 6:266–277. https://doi.org/10.1002/elsc.200520123
Maskow T, Kemp R, Buchholz F et al (2010) What heat is telling us about microbial conversions in nature and technology: from chip- to megacalorimetry. Microb Biotechnol 3:269–284. https://doi.org/10.1111/j.1751-7915.2009.00121.x
Mears L, Stocks SM, Albaek MO et al (2017a) Mechanistic fermentation models for process design, monitoring, and control. Trends Biotechnol 35:914–924. https://doi.org/10.1016/j.tibtech.2017.07.002
Mears L, Stocks SM, Sin G, Gernaey KV (2017b) A review of control strategies for manipulating the feed rate in fed-batch fermentation processes. J Biotechnol 245:34–46. https://doi.org/10.1016/j.jbiotec.2017.01.008
Mitra S, Murthy GS (2022) Bioreactor control systems in the biopharmaceutical industry: a critical perspective. Syst Microbiol Biomanuf 2:91–112. https://doi.org/10.1007/s43393-021-00048-6
Mohan N, Pavan SS, Jayakumar A et al (2022) Real-time metabolic heat-based specific growth rate soft sensor for monitoring and control of high molecular weight hyaluronic acid production by Streptococcus zooepidemicus. Appl Microbiol Biotechnol 106:1079–1095. https://doi.org/10.1007/s00253-022-11760-1
Mondal PP, Galodha A, Verma VK et al (2023) Review on machine learning-based bioprocess optimization, monitoring, and control systems. Biores Technol 370:128523. https://doi.org/10.1016/j.biortech.2022.128523
Moore B, Sanford R, Zhang A (2019) Case study: The characterization and implementation of dielectric spectroscopy (biocapacitance) for process control in a commercial GMP CHO manufacturing process. Biotechnol Prog 35:e2782. https://doi.org/10.1002/btpr.2782
Murugan C, Natarajan P (2019) Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor. J Microbiol Methods 159:5–11. https://doi.org/10.1016/j.mimet.2019.02.002
Narayanan H, Luna MF, Von Stosch M et al (2020) Bioprocessing in the digital age: the role of process models. Biotechnol J 15:1900172. https://doi.org/10.1002/biot.201900172
Nielsen K, Gall D, Jolley M et al (1996) A homogeneous fluorescence polarization assay for detection of antibody to Brucella abortus. J Immunol Methods 195:161–168
Ödman P, Johansen CL, Olsson L et al (2009) On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors. J Biotechnol 144:102–112
Oliveira R, Simutis R, Feyo De Azevedo S (2004) Design of a stable adaptive controller for driving aerobic fermentation processes near maximum oxygen transfer capacity. J Process Control 14:617–626. https://doi.org/10.1016/j.jprocont.2004.01.003
Paulsson D, Gustavsson R, Mandenius C-F (2014) A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals. Sensors 14:17864–17882. https://doi.org/10.3390/s141017864
Peng J, Meng F, Ai Y (2013) Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm. Biores Technol 138:345–352
Pinsach J, De Mas C, López-Santín J (2006) A simple feedback control of Escherichia coli growth for recombinant aldolase production in fed-batch mode. Biochem Eng J 29:235–242. https://doi.org/10.1016/j.bej.2006.01.001
Rathore AS, Mishra S, Nikita S, Priyanka P (2021) Bioprocess control: current progress and future perspectives. Life 11:557. https://doi.org/10.3390/life11060557
Rathore AS, Winkle H (2009) Quality by design for biopharmaceuticals. Nat Biotechnol 27:26–34. https://doi.org/10.1038/nbt0109-26
Reichelt WN, Thurrold P, Brillmann M et al (2016) Generic biomass estimation methods targeting physiologic process control in induced bacterial cultures. Eng Life Sci 16:720–730. https://doi.org/10.1002/elsc.201500182
Reyes SJ, Durocher Y, Pham PL, Henry O (2022) Modern sensor tools and techniques for monitoring, controlling, and improving cell culture processes. Processes 10:189. https://doi.org/10.3390/pr10020189
Rohde M, Paufler S, Harms H, Maskow T (2016) Calorespirometric feeding control enhances bioproduction from toxic feedstocks—demonstration for biopolymer production out of methanol. Biotech Bioeng 113:2113–2121. https://doi.org/10.1002/bit.25986
Rómoli S, Serrano M, Rossomando F et al (2017) Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor. Complexity 2017:1–16. https://doi.org/10.1155/2017/9391879
Schaepe S, Kuprijanov A, Simutis R, Lübbert A (2014) Avoiding overfeeding in high cell density fed-batch cultures of E. coli during the production of heterologous proteins. J Biotechnol 192:146–153. https://doi.org/10.1016/j.jbiotec.2014.09.002
Schuler MM, Marison IW (2012) Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives. Appl Microbiol Biotechnol 94:1469–1482. https://doi.org/10.1007/s00253-012-4095-z
Seborg DE, Edgar TF, Mellichamp DA, Doyle FJ III (2016) Process dynamics and control. John Wiley & Sons
Simutis R, Lübbert A (2015) Bioreactor control improves bioprocess performance. Biotechnol J 10:1115–1130. https://doi.org/10.1002/biot.201500016
Sinner P, Stiegler M, Herwig C, Kager J (2021) Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty. Biores Technol 321:124395. https://doi.org/10.1016/j.biortech.2020.124395
Sommeregger W, Sissolak B, Kandra K et al (2017) Quality by control: towards model predictive control of mammalian cell culture bioprocesses. Biotechnol J 12:1600546. https://doi.org/10.1002/biot.201600546
Soons ZITA, Streefland M, Van Straten G, Van Boxtel AJB (2008) Assessment of near infrared and “software sensor” for biomass monitoring and control. Chemom Intell Lab Syst 94:166–174. https://doi.org/10.1016/j.chemolab.2008.07.009
Soons ZITA, Voogt JA, Van Straten G, Van Boxtel AJB (2006) Constant specific growth rate in fed-batch cultivation of Bordetella pertussis using adaptive control. J Biotechnol 125:252–268. https://doi.org/10.1016/j.jbiotec.2006.03.005
Survyla A, Levisauskas D, Urniezius R, Simutis R (2021) An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes. Comput Struct Biotechnol J 19:5856–5863. https://doi.org/10.1016/j.csbj.2021.10.015
Tavasoli T, Arjmand S, Ranaei Siadat SO et al (2019) A robust feeding control strategy adjusted and optimized by a neural network for enhancing of alpha 1-antitrypsin production in Pichia pastoris. Biochem Eng J 144:18–27. https://doi.org/10.1016/j.bej.2019.01.005
Ulonska S, Waldschitz D, Kager J, Herwig C (2018) Model predictive control in comparison to elemental balance control in an E. coli fed-batch. Chem Eng Sci 191:459–467. https://doi.org/10.1016/j.ces.2018.06.074
Voisard D, Von Stockar U, Marison IW (2002) Quantitative calorimetric investigation of fed-batch cultures of Bacillus sphaericus 1593M. Thermochim Acta 394:99–111. https://doi.org/10.1016/S0040-6031(02)00243-5
Von Stockar U, Maskow T, Liu J et al (2006) Thermodynamics of microbial growth and metabolism: an analysis of the current situation. J Biotechnol 121:517–533. https://doi.org/10.1016/j.jbiotec.2005.08.012
Wainaina S, Taherzadeh MJ (2023) Automation and artificial intelligence in filamentous fungi-based bioprocesses: a review. Biores Technol 369:128421. https://doi.org/10.1016/j.biortech.2022.128421
Wang F, Du G, Li Y, Chen J (2006) Regulation of CCR in the γ-CGTase production from Bacillus macorous by the specific cell growth rate control. Enzyme Microb Technol 39:1279–1285. https://doi.org/10.1016/j.enzmictec.2006.03.014
Warth B, Rajkai G, Mandenius C-F (2010) Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein. J Biotechnol 147:37–45. https://doi.org/10.1016/j.jbiotec.2010.02.023
Wechselberger P, Sagmeister P, Herwig C (2013) Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes. Bioprocess Biosyst Eng 36:1205–1218. https://doi.org/10.1007/s00449-012-0848-4
Yamuna Rani K, Ramachandra Rao V (1999) Control of fermenters—a review. Bioprocess Eng 21:77–88
Ye K, Jin S, Shimizu K (1994) Fuzzy neural network for the control of high cell density cultivation of recombinant Escherichia coli. J Ferment Bioeng 77:663–673. https://doi.org/10.1016/0922-338X(94)90151-1
Yoon SK, Kang WK, Park TH (1994) Fed-batch operation of recombinant Escherichia coli containing trp promoter with controlled specific growth rate. Biotechnol Bioeng 43:995–999
Yüzgeç U, Türker M, Hocalar A (2009) On-line evolutionary optimization of an industrial fed-batch yeast fermentation process. ISA Trans 48:79–92. https://doi.org/10.1016/j.isatra.2008.09.001
Zhang X-C, Visala A, Halme A, Linko P (1994) Functional state modeling and fuzzy control of fed-batch aerobic baker’s yeast process. J Biotechnol 37:1–10. https://doi.org/10.1016/0168-1656(94)90196-1
Zitzmann J, Weidner T, Eichner G et al (2018) Dielectric spectroscopy and optical density measurement for the online monitoring and control of recombinant protein production in stably transformed Drosophila melanogaster S2 cells. Sensors 18:900. https://doi.org/10.3390/s18030900
Acknowledgements
Not applicable
Funding
The authors gratefully acknowledge the financial support (fellowship) from the Department of Science and Technology-Science and Engineering Research Board (DST-SERB), Government of India, for the successful accomplishment of this work (CRG/2019/002882).
Author information
Authors and Affiliations
Contributions
SSP: Conceptualization, Writing—Original draft, Writing—review & editing, Visualization. SS: Conceptualization, Resources, Supervision, Writing—review & editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.
Ethics approval
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Allampalli, S.S.P., Sivaprakasam, S. Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality. World J Microbiol Biotechnol 40, 196 (2024). https://doi.org/10.1007/s11274-024-03993-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11274-024-03993-1