ρ p
Bubble density
τ mix
Bioreactor mixing time
τ fluid
Fluid timescale
τ p
Bubble timescale
References
1. Lara AR, Galindo E, Ramírez OT, Palomares LA (2006) Living with heterogeneities in bioreactors: understanding the effects of environmental gradients on cells. Mol Biotechnol 34
(3):355–381. https://doi.org/10.1385/MB:34:3:355
2. Zieringer J, Takors R (2018) In silico prediction of large-scale microbial production performance: constraints for getting proper data-driven models. Comput Struct Biotechnol J
16:246–256. https://doi.org/10.1016/j.csbj.2018.06.002
3. Breuer M, Lakehal D, Rodi W (1995) Flow around a surface mounted cubical obstacle:
comparison of les rans-results. In: IMACS/COST conference on CFD, 3D complex flows,
Lausanne 1995
4. Larsson G, Törnkvist M, Ståhl Wernersson E, Trägårdh C, Noorman H, Enfors SO (1996)
Substrate gradients in bioreactors: origin and consequences. Bioprocess Eng 14(6):281–289.
https://doi.org/10.1007/BF00369471
5. Buchholz J, Graf M, Freund A, Busche T, Kalinowski J, Blombach B, Takors R (2014) CO 2 /
HCO 3À perturbations of simulated large scale gradients in a scale-down device cause fast
transcriptional responses in Corynebacterium glutamicum. Appl Microbiol Biotechnol 98
(20):8563–8572. https://doi.org/10.1007/s00253-014-6014-y
6. Löffler M, Simen JD, Jäger G, Schäferhoff K, Freund A, Takors R (2016) Engineering E. coli
for large-scale production – strategies considering ATP expenses and transcriptional responses.
Metab Eng 38:73–85. https://doi.org/10.1016/j.ymben.2016.06.008
7. Neubauer P, Häggström L, Enfors S-O (1995) Influence of substrate oscillations on acetate
formation and growth yield in Escherichia coli glucose limited fed-batch cultivations.
Biotechnol Bioeng 47(2):139–146. https://doi.org/10.1002/bit.260470204
8. Oosterhuis NMG, Kossen NWF (1984) Dissolved oxygen concentration profiles in a
production-scale bioreactor. Biotechnol Bioeng 26(5):546–550. https://doi.org/10.1002/bit.
260260522
9. Simen JD, Löffler M, Jäger G, Schäferhoff K, Freund A, Matthes J, Müller J et al (2017)
Transcriptional response of Escherichia coli to ammonia and glucose fluctuations. Microb
Biotechnol 10(4):858–872. https://doi.org/10.1111/1751-7915.12713
10. Teleki A, Sánchez-Kopper A, Takors R (2015) Alkaline conditions in hydrophilic interaction
liquid chromatography for intracellular metabolite quantification using tandem mass spectrometry. Anal Biochem 475:4–13. https://doi.org/10.1016/j.ab.2015.01.002
11. von Wulffen J, Ulmer A, Jäger G, Sawodny O, Feuer R (2017) Rapid sampling of Escherichia
coli after changing oxygen conditions reveals transcriptional dynamics. Genes 8(3). https://doi.
org/10.3390/genes8030090
12. Haringa C, Deshmukh AT, Mudde RF, Noorman HJ (2017a) Euler-Lagrange analysis towards
representative down-scaling of a 22 M3 Aerobic S. cerevisiae fermentation. Chem Eng Sci
170:653–669. https://doi.org/10.1016/j.ces.2017.01.014
13. Haringa C, Mudde RF, Noorman HJ (2018a) From industrial fermentor to CFD-guided
downscaling: what have we learned? Biochem Eng J 140(April):57–71. https://doi.org/10.
1016/j.bej.2018.09.001
14. Haringa C, Noorman HJ, Mudde RF (2017b) Lagrangian modeling of hydrodynamic–kinetic
interactions in (bio)chemical reactors: practical implementation and setup guidelines. Chem
Eng Sci 157:159–168. https://doi.org/10.1016/j.ces.2016.07.031
250
C. S. S. Hajian et al.
Bubble density
τ mix
Bioreactor mixing time
τ fluid
Fluid timescale
τ p
Bubble timescale
References
1. Lara AR, Galindo E, Ramírez OT, Palomares LA (2006) Living with heterogeneities in bioreactors: understanding the effects of environmental gradients on cells. Mol Biotechnol 34
(3):355–381. https://doi.org/10.1385/MB:34:3:355
2. Zieringer J, Takors R (2018) In silico prediction of large-scale microbial production performance: constraints for getting proper data-driven models. Comput Struct Biotechnol J
16:246–256. https://doi.org/10.1016/j.csbj.2018.06.002
3. Breuer M, Lakehal D, Rodi W (1995) Flow around a surface mounted cubical obstacle:
comparison of les rans-results. In: IMACS/COST conference on CFD, 3D complex flows,
Lausanne 1995
4. Larsson G, Törnkvist M, Ståhl Wernersson E, Trägårdh C, Noorman H, Enfors SO (1996)
Substrate gradients in bioreactors: origin and consequences. Bioprocess Eng 14(6):281–289.
https://doi.org/10.1007/BF00369471
5. Buchholz J, Graf M, Freund A, Busche T, Kalinowski J, Blombach B, Takors R (2014) CO 2 /
HCO 3À perturbations of simulated large scale gradients in a scale-down device cause fast
transcriptional responses in Corynebacterium glutamicum. Appl Microbiol Biotechnol 98
(20):8563–8572. https://doi.org/10.1007/s00253-014-6014-y
6. Löffler M, Simen JD, Jäger G, Schäferhoff K, Freund A, Takors R (2016) Engineering E. coli
for large-scale production – strategies considering ATP expenses and transcriptional responses.
Metab Eng 38:73–85. https://doi.org/10.1016/j.ymben.2016.06.008
7. Neubauer P, Häggström L, Enfors S-O (1995) Influence of substrate oscillations on acetate
formation and growth yield in Escherichia coli glucose limited fed-batch cultivations.
Biotechnol Bioeng 47(2):139–146. https://doi.org/10.1002/bit.260470204
8. Oosterhuis NMG, Kossen NWF (1984) Dissolved oxygen concentration profiles in a
production-scale bioreactor. Biotechnol Bioeng 26(5):546–550. https://doi.org/10.1002/bit.
260260522
9. Simen JD, Löffler M, Jäger G, Schäferhoff K, Freund A, Matthes J, Müller J et al (2017)
Transcriptional response of Escherichia coli to ammonia and glucose fluctuations. Microb
Biotechnol 10(4):858–872. https://doi.org/10.1111/1751-7915.12713
10. Teleki A, Sánchez-Kopper A, Takors R (2015) Alkaline conditions in hydrophilic interaction
liquid chromatography for intracellular metabolite quantification using tandem mass spectrometry. Anal Biochem 475:4–13. https://doi.org/10.1016/j.ab.2015.01.002
11. von Wulffen J, Ulmer A, Jäger G, Sawodny O, Feuer R (2017) Rapid sampling of Escherichia
coli after changing oxygen conditions reveals transcriptional dynamics. Genes 8(3). https://doi.
org/10.3390/genes8030090
12. Haringa C, Deshmukh AT, Mudde RF, Noorman HJ (2017a) Euler-Lagrange analysis towards
representative down-scaling of a 22 M3 Aerobic S. cerevisiae fermentation. Chem Eng Sci
170:653–669. https://doi.org/10.1016/j.ces.2017.01.014
13. Haringa C, Mudde RF, Noorman HJ (2018a) From industrial fermentor to CFD-guided
downscaling: what have we learned? Biochem Eng J 140(April):57–71. https://doi.org/10.
1016/j.bej.2018.09.001
14. Haringa C, Noorman HJ, Mudde RF (2017b) Lagrangian modeling of hydrodynamic–kinetic
interactions in (bio)chemical reactors: practical implementation and setup guidelines. Chem
Eng Sci 157:159–168. https://doi.org/10.1016/j.ces.2016.07.031
250
C. S. S. Hajian et al.