Biotechnol Bioeng. 2021 Jul;118(7):2759-2769. doi: 10.1002/bit.27795. Epub 2021 May 03.

Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis.

Biotechnology and bioengineering

Holger Morschett, Niklas Tenhaef, Johannes Hemmerich, Laura Herbst, Markus Spiertz, Deniz Dogan, Wolfgang Wiechert, Stephan Noack, Marco Oldiges

PMID: 33871051 DOI: 10.1002/bit.27795

Abstract

Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.

© 2021 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.

Keywords: laboratory automation; maximum likelihood estimation; process control system; robotic integration; stirred tank bioreactor

References

1. Baumann, P., Hahn, T., & Hubbuch, J. (2015). High-throughput micro-scale cultivations and chromatography modeling: Powerful tools for integrated process development. Biotechnology and Bioengineering, 112(10), 2123-2133. https://doi.org/10.1002/bit.25630 - PubMed
2. Ben-Kiki, O., Evans, C., & döt Net, I. (2009). YAML ain't markup language (YAML™) version 1.2. https://yaml.org/spec/1.2/spec.html - PubMed
3. Bienick, M. S., Young, K. W., Klesmith, J. R., Detwiler, E. E., Tomek, K. J., & Whitehead, T. A. (2014). The Interrelationship between promoter strength, gene expression, and growth rate. PLOS One, 9(10), e109105. https://doi.org/10.1371/journal.pone.0109105 - PubMed
4. Bisswanger, H. (2014). Enzyme assays. Perspectives in Science, 1(1), 41-55. https://doi.org/10.1016/j.pisc.2014.02.005 - PubMed
5. Borkowski, O., Goelzer, A., Schaffer, M., Calabre, M., Mäder, U., Aymerich, S., Jules, M., & Fromion, V. (2016). Translation elicits a growth rate-dependent, genome-wide, differential protein production in Bacillus subtilis. Molecular Systems Biology, 12(5), 870. https://doi.org/10.15252/msb.20156608 - PubMed
6. Chmiel, H. (2011). Bioprozesstechnik. Spektrum Akademischer Verlag. - PubMed
7. Cline, B., Reinartz, B., Lloyd, B., Anthoff, D., Akhiyarov, D., Roberts, T., Uriarte, V., Arvid, J. B., Bremner, C., Burnett, J., Catarino, A., Cronyn, I., Dittrich, M., Dupras, V., Dupré, X., Earl, A., Fernandez, D., Frayne, J., Freitag, S., …Yang, W. (2019). pythonnet. http://pythonnet.github.io/ - PubMed
8. Cruz Bournazou, M. N., Barz, T., Nickel, D. B., Lopez Cárdenas, D. C., Glauche, F., Knepper, A., & Neubauer, P. (2017). Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities. Biotechnology and Bioengineering, 114(3), 610-619. https://doi.org/10.1002/bit.26192 - PubMed
9. Doig, S. D., Baganz, F., & Lye, G. J. (2006). High-throughput screening and process optimisation. In C. Ratledge, & B. Kristiansen (Eds.), Basic biotechnology (3rd ed.) pp. 289-306. Cambridge University Press. - PubMed
10. Funke, M., Buchenauer, A., Schnakenberg, U., Mokwa, W., Diederichs, S., Mertens, A., Müller, C., Kensy, F., & Büchs, J. (2010). Microfluidic BioLector-Microfluidic bioprocess control in microtiter plates. Biotechnology and Bioengineering, 107(3), 497-505. https://doi.org/10.1002/bit.22825 - PubMed
11. Grünberger, A., van Ooyen, J., Paczia, N., Rohe, P., Schiendzielorz, G., Eggeling, L., Wiechert, W., Kohlheyer, D., & Noack, S. (2013). Beyond growth rate 0.6: Corynebacterium glutamicum cultivated in highly diluted environments. Biotechnology and Bioengineering, 110(1), 220-228. https://doi.org/10.1002/bit.24616 - PubMed
12. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., delRío, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. https://doi.org/10.1038/s41586-020-2649-2 - PubMed
13. Hemmerich, J., Labib, M., Steffens, C., Reich, S. J., Weiske, M., Baumgart, M., Rückert, C., Ruwe, M., Siebert, D., Wendisch, V. F., Kalinowski, J., Wiechert, W., & Oldiges, M. (2020). Screening of a genome-reduced Corynebacterium glutamicum strain library for improved heterologous cutinase secretion. Microbial Biotechnology, 13, 2020-2031. https://doi.org/10.1111/1751-7915.13660 - PubMed
14. Hemmerich, J., Moch, M., Jurischka, S., Wiechert, W., Freudl, R., & Oldiges, M. (2018). Combinatorial impact of Sec signal peptides from Bacillus subtilis and bioprocess conditions on heterologous cutinase secretion by Corynebacterium glutamicum. Biotechnology and Bioengineering, 116, 644-655. https://doi.org/10.1002/bit.26873 - PubMed
15. Hemmerich, J., Noack, S., Wiechert, W., & Oldiges, M. (2018). Microbioreactor systems for accelerated bioprocess development. Biotechnology Journal, 13, 1700141. https://doi.org/10.1002/biot.201700141 - PubMed
16. Hemmerich, J., Tenhaef, N., Steffens, C., Kappelmann, J., Weiske, M., Reich, S. J., Wiechert, W., Oldiges, M., & Noack, S. (2018). Less sacrifice, more insight: Repeated low-volume sampling of microbioreactor cultivations enables accelerated deep phenotyping of microbial strain libraries. Biotechnology Journal, 14, 1800428. https://doi.org/10.1002/biot.201800428 - PubMed
17. Hemmerich, J., Tenhaef, N., Wiechert, W., & Noack, S. (2021). pyFOOMB: Python framework for object oriented modelling of bioprocesses. Engineering in Life Sciences, 21(3-4), 242-257. https://doi.org/10.1002/elsc.202000088 - PubMed
18. Heux, S., Poinot, J., Massou, S., Sokol, S., & Portais, J.-C. (2014). A novel platform for automated high-throughput fluxome profiling of metabolic variants. Metabolic Engineering, 25, 8-19. https://doi.org/10.1016/j.ymben.2014.06.001 - PubMed
19. Heyland, J., Blank, L. M., & Schmid, A. (2011). Quantification of metabolic limitations during recombinant protein production in Escherichia coli. Journal of Biotechnology, 155(2), 178-184. https://doi.org/10.1016/j.jbiotec.2011.06.016 - PubMed
20. Huber, R., Ritter, D., Hering, T., Hillmer, A.-K., Kensy, F., Müller, C., Wang, L., & Büchs, J. (2009). Robo-Lector-A novel platform for automated high-throughput cultivations in microtiter plates with high information content. Microbial Cell Factories, 8, 42. https://doi.org/10.1186/1475-2859-8-42 - PubMed
21. Keilhauer, C., Eggeling, L., & Sahm, H. (1993). Isoleucine synthesis in Corynebacterium glutamicum: molecular analysis of the ilvB-ilvN-ilvC operon. Journal of Bacteriology, 175(17), 5595-5603. https://doi.org/10.1128/jb.175.17.5595-5603.1993 - PubMed
22. Kilikian, B. V., Suárez, I. D., Liria, C. W., & Gombert, A. K. (2000). Process strategies to improve heterologous protein production in Escherichia coli under lactose or IPTG induction. Process Biochemistry, 35(9), 1019-1025. https://doi.org/10.1016/S0032-9592(00)00137-0 - PubMed
23. Klumpp, S., Zhang, Z., & Hwa, T. (2009). Growth rate-dependent global effects on gene expression in bacteria. Cell, 139(7), 1366-1375. https://doi.org/10.1016/j.cell.2009.12.001 - PubMed
24. Kreyenschulte, D., Paciok, E., Regestein, L., Blumich, B., & Buchs, J. (2015). Online monitoring of fermentation processes via non-invasive low-field NMR. Biotechnology and Bioengineering, 112(9), 1810-1821. https://doi.org/10.1002/bit.25599 - PubMed
25. Lee, H. L. T., Boccazzi, P., Gorret, N., Ram, R. J., & Sinskey, A. J. (2004). In situ bioprocess monitoring of Escherichia coli bioreactions using Raman spectroscopy. Vibrational Spectroscopy, 35(1), 131-137. https://doi.org/10.1016/j.vibspec.2003.12.015 - PubMed
26. Marques, M. P. C., Cabral, J. M. S., & Fernandes, P. (2010). Bioprocess scale-up: quest for the parameters to be used as criterion to move from microreactors to lab-scale. Journal of Chemical Technology and Biotechnology, 85(9), 1184-1198. https://doi.org/10.1002/jctb.2387 - PubMed
27. Marschall, L., Sagmeister, P., & Herwig, C. (2016). Tunable recombinant protein expression in E. coli: enabler for continuous processing? Applied Microbiology and Biotechnology, 100(13), 5719-5728. https://doi.org/10.1007/s00253-016-7550-4 - PubMed
28. McKinney, W. (2010). Data structures for statistical computing in Python. Austin, TX: Paper presented at the Proceedings of the 9th Python in Science Conference. - PubMed
29. Meissner, D., Vollstedt, A., van Dijl, J. M., & Freudl, R. (2007). Comparative analysis of twin-arginine (Tat)-dependent protein secretion of a heterologous model protein (GFP) in three different Gram-positive bacteria. Applied Microbiology and Biotechnology, 76(3), 633-642. https://doi.org/10.1007/s00253-007-0934-8 - PubMed
30. Paczia, N., Nilgen, A., Lehmann, T., Gätgens, J., Wiechert, W., & Noack, S. (2012). Extensive exometabolome analysis reveals extended overflow metabolism in various microorganisms. Microbial Cell Factories, 11, 122. https://doi.org/10.1186/1475-2859-11-122 - PubMed
31. Passonneau, J. V., & Lowry, O. H. (1993). Enzymatic analysis: A practical guide. Humana Press - PubMed
32. Peuker, T., Riedel, M., Kaiser, C., Ellert, A., Lenz, K., Elsholz, O., & Luttmann, R. (2004). At-line determination of glucose, ammonia, and acetate in high cell density cultivations of Escherichia coli. Engineering in Life Sciences, 4(2), 138-143. https://doi.org/10.1002/elsc.200401910 - PubMed
33. Puskeiler, R., Kaufmann, K., & Weuster-Botz, D. (2005). Development, parallelization, and automation of a gas-inducing milliliter-scale bioreactor for high-throughput bioprocess design (HTBD). Biotechnology and Bioengineering, 89(5), 512-523. https://doi.org/10.1002/bit.20352 - PubMed
34. Rohe, P., Venkanna, D., Kleine, B., Freudl, R., & Oldiges, M. (2012). An automated workflow for enhancing microbial bioprocess optimization on a novel microbioreactor platform. Microbial Cell Factories, 11, 144. https://doi.org/10.1186/1475-2859-11-144 - PubMed
35. Sonnleitner, B., Locher, G., & Fiechter, A. (1992). Biomass determination. Journal of Biotechnology, 25(1), 5-22. https://doi.org/10.1016/0168-1656(92)90107-K - PubMed
36. Tamburini, E., Marchetti, M. G., & Pedrini, P. (2014). Monitoring key parameters in bioprocesses using near-infrared technology. Sensors, 14(10), 18941-18959. https://doi.org/10.3390/s141018941 - PubMed
37. Tohmola, N., Ahtinen, J., Pitkänen, J.-P., Parviainen, V., Joenväärä, S., Hautamäki, M., Lindroos, P., Mäkinen, J., & Renkonen, R. (2011). On-line high performance liquid chromatography measurements of extracellular metabolites in an aerobic batch yeast (Saccharomyces cerevisiae) culture. Biotechnology and Bioprocess Engineering, 16(2), 264-272. https://doi.org/10.1007/s12257-010-0147-3 - PubMed
38. Unthan, S., Grünberger, A., van Ooyen, J., Gätgens, J., Heinrich, J., Paczia, N., Wiechert, W., Kohlheyer, D., & Noack, S. (2014). Beyond growth rate 0.6: What drives Corynebacterium glutamicum to higher growth rates in defined medium. Biotechnology and Bioengineering, 111(2), 359-371. https://doi.org/10.1002/bit.25103 - PubMed
39. Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … SciPy, C. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261-272. https://doi.org/10.1038/s41592-019-0686-2 - PubMed
40. Vojinović, V., Cabral, J. M. S., & Fonseca, L. P. (2006). Real-time bioprocess monitoring: Part I: In situ sensors. Sensors and Actuators B: Chemical, 114(2), 1083-1091. https://doi.org/10.1016/j.snb.2005.07.059 - PubMed
41. van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: A structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22-30. https://doi.org/10.1109/MCSE.2011.37 - PubMed

Publication Types

• Journal Article
• Research Support, Non-U.S. Gov't