location for the product synthesis. It fosters the application of population balances
and their integration into model-based descriptions of scale-up effects.
References
1. Anane E, Sawatzki A, Neubauer P, Cruz Bournazou MN (2019) Modelling concentration
gradients in fed-batch cultivations of E. coli – towards the flexible design of scale-down
experiments. J Chem Technol Biotechnol 94:516–526
2. Neubauer P, Cruz N, Glauche F, Junne S, Knepper A, Raven M (2013) Consistent development of bioprocesses from microliter cultures to the industrial scale. Eng Life Sci 13:224–238
3. Neubauer P, Junne S (2016) Scale-up and scale-down methodologies for bioreactors. In:
Mandenius CF (ed) Bioreactors: design, operation and novel applications. Wiley-VCH Verlag
GmbH, Weinheim, pp 323–354
4. Reitz C, Fan Q, Neubauer P (2018) Synthesis of non-canonical branched-chain amino acids in
Escherichia coli and approaches to avoid their incorporation into recombinant proteins. Curr
Opin Biotechnol 53:248–253
5. Wang G, Haringa C, Tang W, Noorman H, Chu J, Zhuang Y, Zhang S (2020) Coupled
metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses.
Biotechnol Bioeng 117:844–867
6. Grieves M, Vickers J (2016) Digital twin: mitigating unpredictable, undesirable emergent
behavior in complex systems. In: Transdisciplinary perspectives on complex systems: new
findings and approaches, pp 85–113
7. Grossmann I (2005) Enterprise-wide optimization: a new frontier in process systems engineering. AIChE J:1846–1857
8. Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and industry 4.0:
360 degree comparison. IEEE Access 6:3585–3593
9. Batstone DJ, Keller J, Angelidaki I, Kalyuzhnyi SV, Pavlostathis SG, Rozzi A, Sanders WTM,
Siegrist H, Vavilin VA (2002) The IWA anaerobic digestion model no 1(ADM 1). Water Sci
Technol 45:65–73
10. Tsugawa H (2018) Advances in computational metabolomics and databases deepen the
understanding of metabolisms. Curr Opin Biotechnol 54:10
11. Kitano H (2002) Computational systems biology. Nature 420:206–210
12. Stephanopoulos GN, Aristidou AA, Nielsen J (1998) Review of cellular metabolism. In:
Metabolic engineering. Academic Press, San Diego, pp 21–79
13. Varma A, Palsson BO (1994) Metabolic flux balancing: basic concepts, scientific and practical
use. Bio/Technology 12:994–998
14. Marchisio MA, Stelling J (2009) Computational design tools for synthetic biology. Curr Opin
Biotechnol 20:479–485
15. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics.
Bioinformatics 23:2507–2517
16. Bailey JE (1998) Mathematical modeling and analysis in biochemical engineering: past
accomplishments and future opportunities. Biotechnol Prog 14:8–20
17. Koutinas M, Kiparissides A, Pistikopoulos EN, Mantalaris A (2012) Bioprocess systems
engineering: transferring traditional process engineering principles to industrial biotechnology. Comput Struct Biotechnol J 3:e201210022
18. Anane E, López CDC, Barz T, Sin G, Gernaey KV, Neubauer P, Cruz Bournazou MN (2019)
Output uncertainty of dynamic growth models: effect of uncertain parameter estimates on
model reliability. Biochem Eng J 150:107247
Potential of Integrating Model-Based Design of Experiments Approaches and. . .
23
and their integration into model-based descriptions of scale-up effects.
References
1. Anane E, Sawatzki A, Neubauer P, Cruz Bournazou MN (2019) Modelling concentration
gradients in fed-batch cultivations of E. coli – towards the flexible design of scale-down
experiments. J Chem Technol Biotechnol 94:516–526
2. Neubauer P, Cruz N, Glauche F, Junne S, Knepper A, Raven M (2013) Consistent development of bioprocesses from microliter cultures to the industrial scale. Eng Life Sci 13:224–238
3. Neubauer P, Junne S (2016) Scale-up and scale-down methodologies for bioreactors. In:
Mandenius CF (ed) Bioreactors: design, operation and novel applications. Wiley-VCH Verlag
GmbH, Weinheim, pp 323–354
4. Reitz C, Fan Q, Neubauer P (2018) Synthesis of non-canonical branched-chain amino acids in
Escherichia coli and approaches to avoid their incorporation into recombinant proteins. Curr
Opin Biotechnol 53:248–253
5. Wang G, Haringa C, Tang W, Noorman H, Chu J, Zhuang Y, Zhang S (2020) Coupled
metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses.
Biotechnol Bioeng 117:844–867
6. Grieves M, Vickers J (2016) Digital twin: mitigating unpredictable, undesirable emergent
behavior in complex systems. In: Transdisciplinary perspectives on complex systems: new
findings and approaches, pp 85–113
7. Grossmann I (2005) Enterprise-wide optimization: a new frontier in process systems engineering. AIChE J:1846–1857
8. Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and industry 4.0:
360 degree comparison. IEEE Access 6:3585–3593
9. Batstone DJ, Keller J, Angelidaki I, Kalyuzhnyi SV, Pavlostathis SG, Rozzi A, Sanders WTM,
Siegrist H, Vavilin VA (2002) The IWA anaerobic digestion model no 1(ADM 1). Water Sci
Technol 45:65–73
10. Tsugawa H (2018) Advances in computational metabolomics and databases deepen the
understanding of metabolisms. Curr Opin Biotechnol 54:10
11. Kitano H (2002) Computational systems biology. Nature 420:206–210
12. Stephanopoulos GN, Aristidou AA, Nielsen J (1998) Review of cellular metabolism. In:
Metabolic engineering. Academic Press, San Diego, pp 21–79
13. Varma A, Palsson BO (1994) Metabolic flux balancing: basic concepts, scientific and practical
use. Bio/Technology 12:994–998
14. Marchisio MA, Stelling J (2009) Computational design tools for synthetic biology. Curr Opin
Biotechnol 20:479–485
15. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics.
Bioinformatics 23:2507–2517
16. Bailey JE (1998) Mathematical modeling and analysis in biochemical engineering: past
accomplishments and future opportunities. Biotechnol Prog 14:8–20
17. Koutinas M, Kiparissides A, Pistikopoulos EN, Mantalaris A (2012) Bioprocess systems
engineering: transferring traditional process engineering principles to industrial biotechnology. Comput Struct Biotechnol J 3:e201210022
18. Anane E, López CDC, Barz T, Sin G, Gernaey KV, Neubauer P, Cruz Bournazou MN (2019)
Output uncertainty of dynamic growth models: effect of uncertain parameter estimates on
model reliability. Biochem Eng J 150:107247
Potential of Integrating Model-Based Design of Experiments Approaches and. . .
23