19. Muñoz-Tamayo R, Puillet L, Daniel JB, Sauvant D, Martin O, Taghipoor M, Blavy P (2018)
Review: to be or not to be an identifiable model. Is this a relevant question in animal science
modelling? Animal 12:701–712
20. Villaverde AF, Barreiro A, Papachristodoulou A (2016) Structural identifiability of dynamic
systems biology models. PLoS Comput Biol 12:1–22
21. Brubaker TA (1979) Nonlinear parameter estimation. Anal Chem 51:1385A
22. Brun R, Kühni M, Siegrist H, Gujer W, Reichert P (2002) Practical identifiability of ASM2d
parameters – systematic selection and tuning of parameter subsets. Water Res 36:4113–4127
23. Kravaris C, Hahn J, Chu Y (2013) Advances and selected recent developments in state and
parameter estimation. Comput Chem Eng 51:111–123
24. Vajda S, Rabitz H, Walter E, Lecourtier Y (1989) Qualitative and quantitative identifiability
analysis of nonlinear chemical kinetic models. Chem Eng Commun 83:191–219
25. Bellman R, Astrom KJ (1970) On structural identifiability. Math Biosci 7:329–339
26. Cobelli C, DiStefano JJ (1980) Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. Am J Phys 239:R7–R24
27. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J (2009)
Structural and practical identifiability analysis of partially observed dynamical models by
exploiting the profile likelihood. Bioinformatics 25:1923–1929
28. Neubauer P, Cruz-Bournazou MN (2017) Continuous bioprocess development: methods for
control and characterization of the biological system. In: Subramanian G (ed) Continuous
biomanufacturing – innovative technologies and methods. Wiley, Hoboken, pp 1–30
29. Anane E, García ÁC, Haby B, Hans S, Krausch N, Krewinkel M, Hauptmann P, Neubauer P,
Cruz Bournazou MN (2019) A model-based framework for parallel scale-down fed-batch
cultivations in mini-bioreactors for accelerated phenotyping. Biotechnol Bioeng
116:2906–2918
30. Neubauer P, Glauche F, Cruz-Bournazou MN (2017) Editorial: bioprocess development in the
era of digitalization. Eng Life Sci 17:1140–1141
31. Narayanan H, Luna MF, von Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M,
Butté A, Sokolov M (2020) Bioprocessing in the digital age: the role of process models.
Biotechnol J 15:1–10
32. Noorman H (2011) An industrial perspective on bioreactor scale-down: what we can learn
from combined large-scale bioprocess and model fluid studies. Biotechnol J 6:934–943
33. Petsagkourakis P, Sandoval IO, Bradford E, Zhang D, del Rio-Chanona EA (2020) Reinforcement learning for batch bioprocess optimization. Comput Chem Eng 133:106649
34. Mandenius CF, Brundin A (2008) Bioprocess optimization using design-of-experiments
methodology. Biotechnol Prog 24:1191–1203
35. Wechselberger P, Sagmeister P, Herwig C (2013) Model-based analysis on the extractability
of information from data in dynamic fed-batch experiments. Biotechnol Prog 29:285–296
36. Covert MW, Xiao N, Chen TJ, Karr JR (2008) Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24:2044–2050
37. Haringa C, Deshmukh AT, Mudde RF, Noorman HJ (2017) Euler-Lagrange analysis towards
representative down-scaling of a 22 m
3 aerobic S. cerevisiae fermentation. Chem Eng Sci
170:653–669
38. Delvigne F, Goffin P (2014) Microbial heterogeneity affects bioprocess robustness: dynamic
single-cell analysis contributes to understanding of microbial populations. Biotechnol J
9:61–72
39. Barz T, Lopez Cardenas DC, Cruz Bournazou MN, Körkel S, Walter SF (2016) Real-time
adaptive input design for the determination of competitive adsorption isotherms in liquid
chromatography. Comput Chem Eng 94:104–116
40. Cruz Bournazou MN, Barz T, Nickel DB, Lopez Cárdenas DC, Glauche F, Knepper A,
Neubauer P (2017) Online optimal experimental re-design in robotic parallel fed-batch
cultivation facilities. Biotechnol Bioeng 114:610–619
24
P. Neubauer et al.
Review: to be or not to be an identifiable model. Is this a relevant question in animal science
modelling? Animal 12:701–712
20. Villaverde AF, Barreiro A, Papachristodoulou A (2016) Structural identifiability of dynamic
systems biology models. PLoS Comput Biol 12:1–22
21. Brubaker TA (1979) Nonlinear parameter estimation. Anal Chem 51:1385A
22. Brun R, Kühni M, Siegrist H, Gujer W, Reichert P (2002) Practical identifiability of ASM2d
parameters – systematic selection and tuning of parameter subsets. Water Res 36:4113–4127
23. Kravaris C, Hahn J, Chu Y (2013) Advances and selected recent developments in state and
parameter estimation. Comput Chem Eng 51:111–123
24. Vajda S, Rabitz H, Walter E, Lecourtier Y (1989) Qualitative and quantitative identifiability
analysis of nonlinear chemical kinetic models. Chem Eng Commun 83:191–219
25. Bellman R, Astrom KJ (1970) On structural identifiability. Math Biosci 7:329–339
26. Cobelli C, DiStefano JJ (1980) Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. Am J Phys 239:R7–R24
27. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J (2009)
Structural and practical identifiability analysis of partially observed dynamical models by
exploiting the profile likelihood. Bioinformatics 25:1923–1929
28. Neubauer P, Cruz-Bournazou MN (2017) Continuous bioprocess development: methods for
control and characterization of the biological system. In: Subramanian G (ed) Continuous
biomanufacturing – innovative technologies and methods. Wiley, Hoboken, pp 1–30
29. Anane E, García ÁC, Haby B, Hans S, Krausch N, Krewinkel M, Hauptmann P, Neubauer P,
Cruz Bournazou MN (2019) A model-based framework for parallel scale-down fed-batch
cultivations in mini-bioreactors for accelerated phenotyping. Biotechnol Bioeng
116:2906–2918
30. Neubauer P, Glauche F, Cruz-Bournazou MN (2017) Editorial: bioprocess development in the
era of digitalization. Eng Life Sci 17:1140–1141
31. Narayanan H, Luna MF, von Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M,
Butté A, Sokolov M (2020) Bioprocessing in the digital age: the role of process models.
Biotechnol J 15:1–10
32. Noorman H (2011) An industrial perspective on bioreactor scale-down: what we can learn
from combined large-scale bioprocess and model fluid studies. Biotechnol J 6:934–943
33. Petsagkourakis P, Sandoval IO, Bradford E, Zhang D, del Rio-Chanona EA (2020) Reinforcement learning for batch bioprocess optimization. Comput Chem Eng 133:106649
34. Mandenius CF, Brundin A (2008) Bioprocess optimization using design-of-experiments
methodology. Biotechnol Prog 24:1191–1203
35. Wechselberger P, Sagmeister P, Herwig C (2013) Model-based analysis on the extractability
of information from data in dynamic fed-batch experiments. Biotechnol Prog 29:285–296
36. Covert MW, Xiao N, Chen TJ, Karr JR (2008) Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24:2044–2050
37. Haringa C, Deshmukh AT, Mudde RF, Noorman HJ (2017) Euler-Lagrange analysis towards
representative down-scaling of a 22 m
3 aerobic S. cerevisiae fermentation. Chem Eng Sci
170:653–669
38. Delvigne F, Goffin P (2014) Microbial heterogeneity affects bioprocess robustness: dynamic
single-cell analysis contributes to understanding of microbial populations. Biotechnol J
9:61–72
39. Barz T, Lopez Cardenas DC, Cruz Bournazou MN, Körkel S, Walter SF (2016) Real-time
adaptive input design for the determination of competitive adsorption isotherms in liquid
chromatography. Comput Chem Eng 94:104–116
40. Cruz Bournazou MN, Barz T, Nickel DB, Lopez Cárdenas DC, Glauche F, Knepper A,
Neubauer P (2017) Online optimal experimental re-design in robotic parallel fed-batch
cultivation facilities. Biotechnol Bioeng 114:610–619
24
P. Neubauer et al.