A computational framework for building dynamic bioreactor models integrating detailed enzyme kinetics with limited data: beta-ionone production in Saccharomyces cerevisiae as a case study.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	A computational framework for building dynamic bioreactor models integrating detailed enzyme kinetics with limited data: beta-ionone production in Saccharomyces cerevisiae as a case study.
Συγγραφείς:	Sepúlveda-Celis AI; Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile., Agosin E; Sticta Biologicals, Santiago, Chile., Pérez-Correa JR; Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile., Saa PA; Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. pnsaa@uc.cl.; Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. pnsaa@uc.cl.
Πηγή:	Bioprocess and biosystems engineering [Bioprocess Biosyst Eng] 2026 Aug; Vol. 49 (8), pp. 2163-2183. Date of Electronic Publication: 2026 Jul 10.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101088505 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1615-7605 (Electronic) Linking ISSN: 16157591 NLM ISO Abbreviation: Bioprocess Biosyst Eng Subsets: MEDLINE
Imprint Name(s):	Original Publication: Berlin, Germany : Springer-Verlag, 2001-
Ιατρικοί όροι (MeSH):	Saccharomyces cerevisiae/growth & development , Saccharomyces cerevisiae/metabolism , Norisoprenoids/metabolism , Bioreactors , Models, Biological* , Computer Simulation*, Kinetics
Περίληψη:	Kinetic models are useful tools for predicting the dynamic behavior of metabolic systems and to optimize the performance of bioprocesses. However, the difficulty of fitting their parameters to limited data hinders their widespread use. These models have various disparate parameters, are nonlinear, and display complex interactions. To address this challenge, this study introduces a computational framework for constructing bioreactor models integrating detailed enzyme kinetics under data-limited conditions. The framework is illustrated by the construction of a dynamic model that describes the growth kinetics of Saccharomyces cerevisiae and the production of β-ionone, an apocarotenoid extensively utilized in the flavor and fragrance industries, in batch cultivations. The model was initially formulated and described using 78 free kinetic parameters. Through the systematic application of sensitivity and identifiability analyses and reparameterization, the model was reduced to 9-parameter candidate structures. Multi-criteria decision-making techniques were then employed to select a robust model structure. When validated against an independent experimental dataset with condition-specific cofactor adjustment, the final model achieved comparable overall predictive performance compared to the original model structure, with notable improvements for key pathway metabolites, including up to 38% reduction in the normalized mean absolute error for the target product β-ionone. This framework successfully addresses the challenge of constructing predictive kinetic models from limited experiments while maintaining mechanistic interpretability, providing a quantitative tool for identifying metabolic bottlenecks and guiding metabolic engineering interventions. (© 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Competing Interests:	Declarations. Conflict of interest: The authors declare no competing interests.
References:	Nielsen J, Keasling JD (2016) Engineering cellular metabolism. Cell 164:1185–1197. (PMID: 10.1016/j.cell.2016.02.004) López J, Essus K, Kim IK, Pereira R, Herzog J, Siewers V, Nielsen J, Agosin E (2015) Production of β-ionone by combined expression of carotenogenic and plant CCD1 genes in Saccharomyces cerevisiae. Microb Cell Fact 14:84. (PMID: 10.1186/s12934-015-0273-x4464609) López J, Bustos D, Camilo C, Arenas N, Saa PA, Agosin E (2020) Engineering Saccharomyces cerevisiae for the Overproduction of β-Ionone and Its Precursor β-Carotene. Front Bioeng Biotechnol 8:578793. (PMID: 10.3389/fbioe.2020.5787937556307) Hong KK, Nielsen J (2012) Metabolic engineering of Saccharomyces cerevisiae: a key cell factory platform for future biorefineries. Cell Mol Life Sci 69:2671–2690. (PMID: 10.1007/s00018-012-0945-111115109) Tippmann S, Chen Y, Siewers V, Nielsen J (2013) From flavors and pharmaceuticals to advanced biofuels: production of isoprenoids in Saccharomyces cerevisiae. Biotechnol J 8:1435–1444. (PMID: 10.1002/biot.201300028) Meadows AL, Hawkins KM, Tsegaye Y, Antipov E, Kim Y, Raez L, Dahl RH, Tai A, Mahatdejkul-Meadows T, Xu L, Zhao L, Dasika MS, Murber A, Lenihan J, Eng D, Leng JS, Liu CL, Wenger JW, Jiang H, Chao L, Westfall P, Lai J, Ganesan S, Jackson P, Mans R, Platt D, Reber CD, Segraves K, Biber J, Dang S, Baidoo EEK, Shiba Y, Sato S, Tang YJ, Keasling JD (2016) Rewriting yeast central carbon metabolism for industrial isoprenoid production. Nature 537:694–697. (PMID: 10.1038/nature19769) Villadsen J, Nielsen J, Lidén G (2011) Bioreaction Engineering Principles, 3rd edn. Springer, New York. (PMID: 10.1007/978-1-4419-9688-6) Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. (PMID: 10.1038/nbt.16143108565) Lu H, Li F, Sánchez BJ, Zhu Z, Li G, Vber I, Oberg PM, Zhang J, Siewers V, Nielsen J (2019) A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 10:3586. (PMID: 10.1038/s41467-019-11581-36687777) Sánchez BJ, Nielsen J (2015) Genome scale models of yeast: towards standardized evaluation and consistent omic integration. Integr Biol 7:846–858. (PMID: 10.1039/C5IB00083A) Kim OD, Rohr M, Mahadevan R (2018) A review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering. Front Microbiol 9:1690. (PMID: 10.3389/fmicb.2018.016906079213) Ashyraliyev M, Fomekong-Nanfack Y, Kaandorp JA, Blom JG (2009) Systems biology: parameter estimation for biochemical models. FEBS J 276:886–902. (PMID: 10.1111/j.1742-4658.2008.06844.x) 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. (PMID: 10.1093/bioinformatics/btp358) Stigter JD, Molenaar J (2015) A fast algorithm to assess local structural identifiability. Automatica 58:118–124. https://doi.org/10.1016/j.automatica.2015.05.004. (PMID: 10.1016/j.automatica.2015.05.004) Sánchez BJ, Soto DC, Jorquera H, Gelmi CA, Pérez-Correa JR (2014) HIPPO: An iterative reparametrization method for identification and calibration of dynamic bioreactor models of complex processes. Ind Eng Chem Res 53(48):18514–18525. https://doi.org/10.1021/ie501298basd. (PMID: 10.1021/ie501298basd) Luong JHT (1985) Kinetics of ethanol inhibition in alcohol fermentation. Biotechnol Bioeng 27:280–285. (PMID: 10.1002/bit.260270311) Kompala DS, Ramkrishna D, Tsao GT (1984) Cybernetic modeling of microbial growth on multiple substrates. Biotechnol Bioeng 26(11):1272–1281. https://doi.org/10.1002/bit.260261103. (PMID: 10.1002/bit.260261103) Ghose TK, Tyagi RD (1979) Rapid ethanol fermentation of cellulose hydrolysate. II. Product and substrate inhibition and optimization of fermentor design. Biotechnol Bioeng 21:1401–1420. (PMID: 10.1002/bit.260210808) van Bodegom P (2007) Microbial maintenance: a critical review on its quantification. Microb Ecol 53:513–523. (PMID: 10.1007/s00248-006-9049-51915598) Provost A, Bastin G (2004) Dynamic metabolic modelling under the balanced growth condition. J Process Control 14(7):717–728. https://doi.org/10.1016/j.jprocont.2003.12.004. (PMID: 10.1016/j.jprocont.2003.12.004) Ithayaraja M, Janardan N, Wierenga RK, Savithri HS, Murthy MR (2016) Crystal structure of a thiolase from Escherichia coli at 1.8 Å resolution. Acta crystallographica. Section F, Structural biology communications, 72(Pt 7), 534–544. https://doi.org/10.1107/S2053230X16008451. Nagegowda DA, Bach TJ, Chye ML (2004) Brassica juncea 3-hydroxy-3-methylglutaryl (HMG)-CoA synthase 1: expression and characterization of recombinant wild-type and mutant enzymes. Biochem J 383(Pt 3517–527. https://doi.org/10.1042/BJ20040721. Kim DY, Stauffacher CV, Rodwell VW (2000) Engineering of Sulfolobus solfataricus HMG-CoA reductase to a form whose activity is regulated by phosphorylation and dephosphorylation. Biochemistry 39(9):2269–2275. https://doi.org/10.1021/bi991749t. (PMID: 10.1021/bi991749t) Pak VV, Koo M, Kim MJ, Yang HJ, Yun L, Kwon DY (2008) Modeling an active conformation for linear peptides and design of a competitive inhibitor for HMG-CoA reductase. J Mol recognition: JMR 21(4):224–232. https://doi.org/10.1002/jmr.889. (PMID: 10.1002/jmr.889) Weaver LJ, Sousa MML, Wang G, Baidoo E, Petzold CJ, Keasling JD (2015) A kinetic-based approach to understanding heterologous mevalonate pathway function in E. coli. Biotechnol Bioeng 112(1):111–119. https://doi.org/10.1002/bit.25323. (PMID: 10.1002/bit.25323) Bazaes S, Beytía E, Jabalquinto AM, de Ovando Solís, Gómez F, I., Eyzaguirre J (1980) Pig liver phosphomevalonate kinase. 2. Participation of cysteinyl and lysyl groups in catalysis. Biochemistry 19(11):2305–2310. https://doi.org/10.1021/bi00552a004. (PMID: 10.1021/bi00552a004) Krepkiy D, Miziorko HM (2004) Identification of active site residues in mevalonate diphosphate decarboxylase: implications for a family of phosphotransferases. Protein science: publication Protein Soc 13(7):1875–1881. https://doi.org/10.1110/ps.04725204. (PMID: 10.1110/ps.04725204) Ramos-Valdivia AC, van der Heijden R, Verpoorte R, Camara B (1997) Purification and characterization of two isoforms of isopentenyl-diphosphate isomerase from elicitor-treated Cinchona robusta cells. Eur J Biochem 249(1):161–170. https://doi.org/10.1111/j.1432-1033.1997.t01-1-00161.x. (PMID: 10.1111/j.1432-1033.1997.t01-1-00161.x) Chen H, Li M, Liu C, Zhang H, Xian M, Liu H (2018) Enhancement of the catalytic activity of Isopentenyl diphosphate isomerase (IDI) from Saccharomyces cerevisiae through random and site-directed mutagenesis. Microb Cell Fact 17(1):65. https://doi.org/10.1186/s12934-018-0913-z. (PMID: 10.1186/s12934-018-0913-z5925831) Ferriols VM, Yaginuma R, Adachi M, Takada K, Matsunaga S, Okada S (2015) Cloning and characterization of farnesyl pyrophosphate synthase from the highly branched isoprenoid producing diatom Rhizosolenia setigera. Sci Rep 5:10246. https://doi.org/10.1038/srep10246. (PMID: 10.1038/srep102464440519) Chang TH, Guo RT, Ko TP, Wang AH, Liang PH (2006) Crystal structure of type-III geranylgeranyl pyrophosphate synthase from Saccharomyces cerevisiae and the mechanism of product chain length determination. J Biol Chem 281(21):14991–15000. https://doi.org/10.1074/jbc.M512886200. (PMID: 10.1074/jbc.M512886200) Mookhtiar KA, Kalinowski SS, Zhang D, Poulter CD (1994) Yeast squalene synthase: A mechanism for addition of substrates and activation by NADPH. J Biol Chem 269 1511201–11207. https://doi.org/10.1016/S0021-9258(19)78111-3. Abe I, Abe T, Lou W, Masuoka T, Noguchi H (2007) Site-directed mutagenesis of conserved aromatic residues in rat squalene epoxidase. Biochem Biophys Res Commun 352(1):259–263. https://doi.org/10.1016/j.bbrc.2006.11.014. (PMID: 10.1016/j.bbrc.2006.11.014) Iwata-Reuyl D, Math SK, Desai SB, Poulter CD (2003) Bacterial phytoene synthase: molecular cloning, expression, and characterization of Erwinia herbicola phytoene synthase. Biochemistry 42(11):3359–3365. https://doi.org/10.1021/bi0206614. (PMID: 10.1021/bi0206614) Schnurr G, Misawa N, Sandmann S (1996) Expression, purification and properties of lycopene cyclase from Erwinia uredovora. Biochem J 315(3):869–874. https://doi.org/10.1042/bj3150869. (PMID: 10.1042/bj31508691217287) Schaub P, Yu Q, Gemmecker S, Poussin-Courmontagne P, Mailliot J, McEwen AG, Ghisla R, Beyer P, Schmid SR, Kräutler B (2012) On the structure and function of the phytoene desaturase CRTI from Pantoea ananatis, a membrane-peripheral and FAD-dependent oxidase/isomerase. PLoS ONE 6e39550. https://doi.org/10.1371/journal.pone.0039550. Ke K, Zhang Y, Wang X, Luo Z, Chen Y, Fang X, Zhao L (2025) Identification, Cloning, and Functional Characterization of Carotenoid Cleavage Dioxygenase (CCD) from Olea europaea and Ipomoea nil. Biology 14(7):752. https://doi.org/10.3390/biology14070752. (PMID: 10.3390/biology1407075212292525) Milo R, Jorgensen P, Moran U, Weber G, Springer M (2010) BioNumbers–the database of key numbers in molecular and cell biology. Nucleic Acids Res D750–D753. https://doi.org/10.1093/nar/gkp889. Engel SR, Aleksander S, Nash RS, Wong ED, Weng S, Miyasato SR, Sherlock G, Cherry JM (2025) Saccharomyces Genome Database: advances in genome annotation, expanded biochemical pathways, and other key enhancements. Genetics 229(3):iyae185. https://doi.org/10.1093/genetics/iyae185. (PMID: 10.1093/genetics/iyae18511912841) Bouwknegt J, Koster CC, Vos AM, de Groot MJL (2021) Class-II dihydroorotate dehydrogenases from three phylogenetically distant fungi support anaerobic pyrimidine biosynthesis. Fungal Biology Biotechnol 8(1):10. https://doi.org/10.1186/s40694-021-00117-4. (PMID: 10.1186/s40694-021-00117-4) Papagianni M, Boonpooh Y, Mattey M, Kristiansen B (2007) Substrate inhibition kinetics of Saccharomyces cerevisiae in fed-batch cultures operated at constant glucose and maltose concentration levels. J Ind Microbiol Biotechnol 34(4):301–309. https://doi.org/10.1007/s10295-006-0198-9. (PMID: 10.1007/s10295-006-0198-9) Toda K, Yabe I, Yamagata T (1980) Kinetics of biphasic growth of yeast in continuous and fed-batch cultures. Biotechnol Bioeng 22 91805–1827. https://doi.org/10.1002/bit.260220904. Egea J, Balsa-Canto E (2009) Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind Eng Chem Res 48(9):4388–4401. https://doi.org/10.1021/ie801717t. (PMID: 10.1021/ie801717t) Egea JA, Rodríguez-Fernández M, Banga JR et al (2007) Scatter search for chemical and bio-process optimization. J Glob Optim 37:481–503. https://doi.org/10.1007/s10898-006-9075-3. (PMID: 10.1007/s10898-006-9075-3) Cao J, Wang L, Xu J (2011) Robust estimation for ordinary differential equation models. Biometrics 67(4):1305–1313. https://doi.org/10.1111/j.1541-0420.2011.01577.x. (PMID: 10.1111/j.1541-0420.2011.01577.x) Saitua F, Torres P, Pérez-Correa JR et al (2017) Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris. BMC Syst Biol 11:27. https://doi.org/10.1186/s12918-017-0408-2. (PMID: 10.1186/s12918-017-0408-25320773) Hao H, Zak D, Sauter T, Schwaber J, Ogunnaike B (2006) Modeling the VPAC2-activated cAMP/PKA signaling pathway: From receptor to circadian clock gene induction. Biophys J 90:1560–1571. https://doi.org/10.1529/biophysj.105.065250. (PMID: 10.1529/biophysj.105.065250) Petersen B, Gernaey K, Vanrolleghem PA (2001) Practical identifiability of model parameters by combined respirometric-titrimetric measurements. Water Sci Technol 43(7):347–355. https://doi.org/10.2166/wst.2001.0444. (PMID: 10.2166/wst.2001.0444) Landaw EM, DiStefano JJ III (1984) Multiexponential, multicompartmental, and noncompartmental modeling. II. Data analysis and statistical considerations. Am J Physiol 246(5):R665–R677. https://doi.org/10.1152/ajpregu.1984.246.5.R665. (PMID: 10.1152/ajpregu.1984.246.5.R665) Jaqaman K, Danuser G (2006) Linking data to models: data regression. Nat Rev Mol Cell Biol 7(11):813–819. https://doi.org/10.1038/nrm2030. (PMID: 10.1038/nrm2030) Miao H, Xia X, Perelson AS, Wu H, ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS (2011) SIAM Rev 53(1):3–39. https://doi.org/10.1137/090757009. (PMID: 10.1137/090757009) Wang Z, Rangaiah GP (2017) Ind Eng Chem Res 56(2):560–574. https://doi.org/10.1021/acs.iecr.6b03453 . Application and analysis of methods for selecting an optimal solution from the Pareto-optimal front obtained by multi objective optimization. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 30(1):79–82. https://doi.org/10.3354/cr030079. (PMID: 10.3354/cr030079) Anderson TW, Darling DA (1952) Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Ann Math Stat 23(2):193–212. https://doi.org/10.1214/aoms/1177729437. (PMID: 10.1214/aoms/1177729437) Durbin J, Watson GS (1950) Testing for serial correlation in least squares regression. I Biometrika 37(3–4):409–428. https://doi.org/10.1093/biomet/37.3-4.409. (PMID: 10.1093/biomet/37.3-4.409) Helton JC, Johnson JD, Sallaberry CJ, Storlie CB (2006) Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab Eng Syst Saf 91(10–11):1175–1209. https://doi.org/10.1016/j.ress.2005.11.017. (PMID: 10.1016/j.ress.2005.11.017) Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007) Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 3(10):e189. https://doi.org/10.1371/journal.pcbi.0030189. (PMID: 10.1371/journal.pcbi.00301892000971) Elizondo B, Saa PA (2025) Complex kinetic models predict β-carotene production and reveal flux limitations in recombinant Saccharomyces cerevisiae strains. ACS Synth Biol 14(9):3457–3472. https://doi.org/10.1021/acssynbio.5c00256. (PMID: 10.1021/acssynbio.5c0025612455641) Liebermeister W, Uhlendorf J, Klipp E (2010) Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation. Bioinformatics 26:1528–1534. https://doi.org/10.1093/bioinformatics/btq141. (PMID: 10.1093/bioinformatics/btq141) Saa PA, Nielsen LK (2017) Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnol Adv 35:981–1003. https://doi.org/10.1016/j.biotechadv.2017.09.005. (PMID: 10.1016/j.biotechadv.2017.09.005) Saa PA, Nielsen LK (2016) Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach. Sci Rep 6:29635. https://doi.org/10.1038/srep29635. (PMID: 10.1038/srep296354945864) Jordá T, Puig S (2020) Regulation of Ergosterol Biosynthesis in Saccharomyces cerevisiae. Genes 11(7):795. https://doi.org/10.3390/genes11070795. (PMID: 10.3390/genes110707957397035) Ahrazem O, Gómez-Gómez L, Rodrigo MJ, Avalos J, Limón MC (2016) Carotenoid Cleavage Oxygenases from Microbes and Photosynthetic Organisms: Features and Functions. Int J Mol Sci 17(11):1781. https://doi.org/10.3390/ijms17111781. (PMID: 10.3390/ijms171117815133782)
Grant Information:	CIA 250013 Agencia Nacional de Investigación y Desarrollo
Contributed Indexing:	Keywords: Saccharomyces cerevisiae; Beta-ionone; Kinetic modeling; Multi-criteria decision analysis; Parameter identifiability
Substance Nomenclature:	A7NRR1HLH6 (beta-ionone) 0 (Norisoprenoids)
Entry Date(s):	Date Created: 20260710 Date Completed: 20260730 Latest Revision: 20260730
Update Code:	20260730
DOI:	10.1007/s00449-026-03384-w
PMID:	42429962
Βάση Δεδομένων:	MEDLINE

View record at Springer

καταχωρήστε σχόλιο πρώτοι!