Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala

Ilustraciones

Autores:: Moreno Otálvaro, Sebastián

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_c758622406b64824afdbb1b991248396
oai_identifier_str	oai:repositorio.unal.edu.co:unal/84402
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
dc.title.translated.eng.fl_str_mv	Evaluation of the impact of concentration gradients on the microbial carbon central metabolism by a multiscale modelling approach
title	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
spellingShingle	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Bioreactores Control de procesos biotecnológicos Modelamiento multiescala Modelo de compartimentos Modelo cinético Carbohidratos de reserva Metabolismo de carbono central Saccharomyces cerevisiae Multiscale modeling Compartment model Kinetic model Storage carbohydrates Central carbon metabolism
title_short	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
title_full	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
title_fullStr	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
title_full_unstemmed	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
title_sort	Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala
dc.creator.fl_str_mv	Moreno Otálvaro, Sebastián
dc.contributor.advisor.none.fl_str_mv	Suárez Méndez, Camilo Alberto
dc.contributor.author.none.fl_str_mv	Moreno Otálvaro, Sebastián
dc.contributor.researchgroup.spa.fl_str_mv	Bioprocesos y Flujos Reactivos
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Bioreactores Control de procesos biotecnológicos Modelamiento multiescala Modelo de compartimentos Modelo cinético Carbohidratos de reserva Metabolismo de carbono central Saccharomyces cerevisiae Multiscale modeling Compartment model Kinetic model Storage carbohydrates Central carbon metabolism
dc.subject.lemb.none.fl_str_mv	Bioreactores Control de procesos biotecnológicos
dc.subject.proposal.spa.fl_str_mv	Modelamiento multiescala Modelo de compartimentos Modelo cinético Carbohidratos de reserva Metabolismo de carbono central
dc.subject.proposal.other.fl_str_mv	Saccharomyces cerevisiae
dc.subject.proposal.eng.fl_str_mv	Multiscale modeling Compartment model Kinetic model Storage carbohydrates Central carbon metabolism
description	Ilustraciones
publishDate	2022
dc.date.issued.none.fl_str_mv	2022-12-04
dc.date.accessioned.none.fl_str_mv	2023-08-01T19:19:52Z
dc.date.available.none.fl_str_mv	2023-08-01T19:19:52Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/84402
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84402 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Antoniewicz, M. R. (2021). A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications. Metabolic Engineering, 63(November 2020), 2–12. https://doi.org/10.1016/j.ymben.2020.11.002 Ascanio, G. (2015). Mixing time in stirred vessels: A review of experimental techniques. Chinese Journal of Chemical Engineering, 23(7), 1065–1076. https://doi.org/10.1016/j.cjche.2014.10.022 Baskaran, S. (2010). Structure and Regulation of Yeast Glycogen Synthase (Issue August) [Indiana University]. https://doi.org/10.1007/978-3-319-43589-3_1 Bond, C. J., Jurica, M. S., Mesecar, A., & Stoddard, B. L. (2000). Determinants of allosteric activation of yeast pyruvate kinase and identification of novel effectors using computational screening. Biochemistry, 39(50), 15333–15343. https://doi.org/10.1021/bi001443i Chan, C. Y., & Parra, K. J. (2014). Yeast phosphofructokinase-1 subunit Pfk2p is necessary for pH homeostasis and glucose-dependent vacuolar ATPase reassembly. Journal of Biological Chemistry, 289(28), 19448–19457. https://doi.org/10.1074/jbc.M114.569855 Charpentier, J. C. (2009). Perspective on multiscale methodology for product design and engineering. Computers and Chemical Engineering, 33(5), 936–946. https://doi.org/10.1016/j.compchemeng.2008.11.007 Cleland, W. W. (1963a). Biochimica Et Biophysica Acta the Kinetics of Enzyme-Catalyzed Re Ti With Two or More Substrates or Pr D Ct I. Nomen Clature a Td Rate Equatio. Biochirn. Biophys. Acta, 67(2), 67. https://doi.org/10.1016/0926-6569(63)90211-6 Cleland, W. W. (1963b). The kinetics of enzyme-catalyzed reactions with two or more substrates or products. II. Inhibition: Nomenclature and theory. BBA - Biochimica et Biophysica Acta, 67(C), 173–187. https://doi.org/10.1016/0006-3002(63)91815-8 Cui, Y. Q., Van der Lans, R. G. J. M., & Luyben, K. C. A. M. (1996). Local Power Uptake in Gas-Liquid Systems With Single and Multiple Rushton Turbines. 51(1), 2631– 2636. Doran, P. M. (2012). Bioprocess engineering principles: Second edition. In Bioprocess Engineering Principles: Second Edition (Vol. 9780080917). Flamholz, A. (2018). eQuilibrator. https://equilibrator.weizmann.ac.il/ François, J., & Parrou, J. L. (2001). Reserve carbohydrates metabolism in the yeast Saccharomyces cerevisiae. FEMS Microbiology Reviews, 25(1), 125–145. https://doi.org/10.1016/S0168-6445(00)00059-0 Gao, H., & Leary, J. A. (2003). Multiplex inhibitor screening and kinetic constant determinations for yeast hexokinase using mass spectrometry based assays. Journal of the American Society for Mass Spectrometry, 14(3), 173–181. https://doi.org/10.1016/S1044-0305(02)00867-X Geerlof, A., Travers, F., Barman, T., & Lionne, C. (2005). Perturbation of yeast 3- phosphoglycerate kinase reaction mixtures with ADP: Transient kinetics of formation of ATP from bound 1,3-bisphosphoglycerate. Biochemistry, 44(45), 14948–14955. https://doi.org/10.1021/bi0512290 Gelves, R., Dietrich, A., & Takors, R. (2014). Modeling of gas-liquid mass transfer in a stirred tank bioreactor agitated by a Rushton turbine or a new pitched blade impeller. Bioprocess and Biosystems Engineering, 37(3), 365–375. https://doi.org/10.1007/s00449-013-1001-8 Gikas, P., & Livingston, A. G. (1998). Use of specific ATP concentration and specific oxygen uptake rate to determine parameters of a structured model of biomass growth. Enzyme and Microbial Technology, 22(6), 500–510. https://doi.org/10.1016/S0141-0229(97)00242-1 Hahn, J. (2020). From Parts to the Whole A Whole-Cell Model for Saccharomyces cerevisiae. Humboldt-university Berlin Hajian, C. S. S., Haringa, C., Noorman, H., & Takors, R. (2020). Predicting by-product gradients of baker’s yeast production at industrial scale: A practical simulation approach. Processes, 8(12), 1–19. https://doi.org/10.3390/pr8121554 Haringa, C., Tang, W., Deshmukh, A. T., Xia, J., Reuss, M., Heijnen, J. J., Mudde, R. F., & Noorman, H. J. (2016). Euler-Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines. Engineering in Life Sciences, 16(7), 652–663. https://doi.org/10.1002/elsc.201600061 Haringa, C., Tang, W., Wang, G., Deshmukh, A. T., van Winden, W. A., Chu, J., van Gulik, W. M., Heijnen, J. J., Mudde, R. F., & Noorman, H. J. (2018). Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization. Chemical Engineering Science, 175, 12–24. https://doi.org/10.1016/j.ces.2017.09.020 Hess, B., & Plesser, T. (1979). Temporal and Spatial Order in Biochemical Systems. Annals of the New York Academy of Sciences, 316(1), 203–213. https://doi.org/10.1111/j.1749-6632.1979.tb29470.x Hori, K., & Unno, H. (2011). Integrated Production and Separation. In Comprehensive Biotechnology, Second Edition (Second Edi, Vol. 2). Elsevier B.V. https://doi.org/10.1016/B978-0-08-088504-9.00116-1 Ingram, G. D., Cameron, I. T., & Hangos, K. M. (2004). Classification and analysis of integrating frameworks in multiscale modelling. Chemical Engineering Science, 59(11), 2171–2187. https://doi.org/10.1016/j.ces.2004.02.010 Johnson, K. A., & Goody, R. S. (2011). The original Michaelis constant: Translation of the 1913 Michaelis-Menten Paper. Biochemistry, 50(39), 8264–8269. https://doi.org/10.1021/bi201284u Jourdan, N., Neveux, T., Potier, O., Kanniche, M., Wicks, J., Nopens, I., Rehman, U., & Le Moullec, Y. (2019). Compartmental Modelling in chemical engineering: A critical review. Chemical Engineering Science, 210, 115196. https://doi.org/10.1016/j.ces.2019.115196 Kesten, D., Kummer, U., Sahle, S., & Hübner, K. (2015). A new model for the aerobic metabolism of yeast allows the detailed analysis of the metabolic regulation during glucose pulse. Biophysical Chemistry, 206, 40–57. https://doi.org/10.1016/j.bpc.2015.06.010 King, E. L., & Altman, C. (1956). A schematic method of deriving the rate laws for enzyme-catalyzed reactions. Journal of Physical Chemistry, 60(10), 1375–1378. https://doi.org/10.1021/j150544a010 Klipp, E., Herwig, P., Kowald, A., Wierling, C., & Lehrach, H. (2005). Systems Biology in Practice. Lam, C. F., & Priest, D. G. (1972). Enzyme Kinetics: Systematic Generation of Valid KingAltman Patterns. Biophysical Journal, 12(3), 248–256. https://doi.org/10.1016/S0006-3495(72)86084-3 Lapin, A., Schmid, J., & Reuss, M. (2006). Modeling the dynamics of E. coli populations in the three-dimensional turbulent field of a stirred-tank bioreactor-A structuredsegregated approach. Chemical Engineering Science, 61(14), 4783–4797. https://doi.org/10.1016/j.ces.2006.03.003 Larsson, C., Påhlman, I. L., & Gustafsson, L. (2000). The importance of ATP as a regulator of glycolytic flux in Saccharomyces cerevisiae. Yeast, 16(9), 797–809. https://doi.org/10.1002/1097-0061(20000630)16:9<797::AID-YEA553>3.0.CO;2-5 Larsson, M., & Arvidsson, L. (1971). Inhibition of Phosphoglycerate Kinase by Products and Product Homologues. 22, 506–512. Leskovac, V. (2004). COMPREHENSIVE ENZYME KINETICS. Kluwer Academic Publishers, 438. https://doi.org/10.1007/0-306-48390-4_5 Luo, Y., Kurian, V., & Ogunnaike, B. A. (2021). Bioprocess systems analysis, modeling, estimation, and control. Current Opinion in Chemical Engineering, 33, 100705. https://doi.org/10.1016/j.coche.2021.100705 Mayr, B., Horvat, P., Nagy, E., & Moser, A. (1993). Mixing-models applied to industrial batch bioreactors. Bioprocess Engineering, 9(1), 1–12. https://doi.org/10.1007/BF00389534 Messiha, H., Kent, E., Malys, N., Carroll, K., Mendes, P., & Smallbone, K. (2014). Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast. PeerJ PrePrints, April. https://doi.org/10.7287/peerj.preprints.146v4 Moisset, P., Vaisman, D., Cintolesi, A., Urrutia, J., Rapaport, I., Andrews, B. A., & Asenjo, J. A. (2012). Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters. Biotechnology and Bioengineering, 109(9), 2325–2339. https://doi.org/10.1002/bit.24503 Monod, J., Wyman, J., & Changeux, J. P. (1965). On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology, 12(1), 88–118. https://doi.org/10.1016/S0022-2836(65)80285-6 Morchain, J. (2017a). Bioreactor Modeling. In Bioreaction Engineering Principles. Elsevier. https://doi.org/10.1007/978-1-4757-4645-7_9 Morchain, J. (2017b). Numerical Tools for Scaling Up Bioreactors. Current Developments in Biotechnology and Bioengineering: Bioprocesses, Bioreactors and Controls, 495– 523. https://doi.org/10.1016/B978-0-444-63663-8.00017-3 Mulcahy, P., O’Flaherty, M., Jennings, L., & Griffin, T. (2002). Application of kinetic-based biospecific affinity chromatographic systems to ATP-dependent enzymes: Studies with yeast hexokinase. Analytical Biochemistry, 309(2), 279–292. https://doi.org/10.1016/S0003-2697(02)00307-X Muloiwa, M., Nyende-Byakika, S., & Dinka, M. (2020). Comparison of unstructured kinetic bacterial growth models. South African Journal of Chemical Engineering, 33(July), 141–150. https://doi.org/10.1016/j.sajce.2020.07.006 Nadal-Rey, G., McClure, D. D., Kavanagh, J. M., Cornelissen, S., Fletcher, D. F., & Gernaey, K. V. (2021). Understanding gradients in industrial bioreactors. Biotechnology Advances, 46(October 2020), 107660. https://doi.org/10.1016/j.biotechadv.2020.107660 Neet, K. E. (1995). Cooperativity in enzyme function: Equilibrium and kinetic aspects. Methods in Enzymology, 249(C), 519–567. https://doi.org/10.1016/0076- 6879(95)49048-5 Nielsen, J. (2014). Bioreaction Engineering Principles. In Psychological Science (Vol. 25, Issue 9). Nienow, A. W. (1998). Hydrodynamics of stirred bioreactors. Applied Mechanics Reviews, 51(1), 3–32. https://doi.org/10.1115/1.3098990 Noorman, H. J., & Heijnen, J. J. (2017). Biochemical engineering’s grand adventure. Chemical Engineering Science, 170, 677–693. https://doi.org/10.1016/j.ces.2016.12.065 Oide, S., & Inui, M. (2017). Trehalose acts as a uridine 5′-diphosphoglucose-competitive inhibitor of trehalose 6-phosphate synthase in Corynebacterium glutamicum. FEBS Journal, 284(24), 4298–4313. https://doi.org/10.1111/febs.14309 Papagianni, M. (2012). Recent advances in engineering the central carbon metabolism of industrially important bacteria. Microbial Cell Factories, 11, 1–13. https://doi.org/10.1186/1475-2859-11-50 Pigou, M., & Morchain, J. (2015). Investigating the interactions between physical and biological heterogeneities in bioreactors using compartment, population balance and metabolic models. Chemical Engineering Science, 126(April), 267–282. https://doi.org/10.1016/j.ces.2014.11.035 Purich, D. L. (2009). Contemporary Enzyme Kinetics and Mechanism: Reliable Lab Solutions. http://books.google.com/books?hl=en&lr=&id=Pr3V80HZLpkC&pgis=1 Richter, O., Betz, A., & Giersch, C. (1975). The response of oscillating glycolysis to perturbations in the NADH/NAD system: A comparison between experiments and a computer model. BioSystems, 7(1), 137–146. https://doi.org/10.1016/0303- 2647(75)90051-9 Rizzi, M., Baltes, M., Theobald, U., & Reuss, M. (1997). In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnology and Bioengineering, 55(4), 592–608. https://doi.org/10.1002/(SICI)1097- 0290(19970820)55:4<592::AID-BIT2>3.0.CO;2-C Rothman, L. B., & Cabib, E. (1967a). Allosteric Properties of Yeast Glycogen Synthetase. I. General Kinetic Study. Biochemical and Biophysical Research Communications, 6(7), 644–650. https://doi.org/10.1016/0006-291X(66)90503-1 Rothman, L. B., & Cabib, E. (1967b). Allosteric Properties of Yeast Glycogen Synthetase. II. The Effect of pH on Inhibition and Its Physiological Implications. Biochemistry, 6(7), 2107–2112. Rudolph, F. B., & Fromm, H. J. (1971). Computer simulation studies with yeast hexokinase and additional evidence for the random Bi Bi mechanism. Journal of Biological Chemistry, 246(21), 6611–6619. https://doi.org/10.1016/s0021- 9258(19)34158-4 Saa, P. A., & Nielsen, L. K. (2017). Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnology Advances, 35(8), 981–1003. https://doi.org/10.1016/j.biotechadv.2017.09.005 Siebler, F., Lapin, A., Hermann, M., & Takors, R. (2019). The impact of CO gradients on C. ljungdahlii in a 125 m3 bubble column: Mass transfer, circulation time and lifeline analysis. Chemical Engineering Science, 207, 410–423. https://doi.org/10.1016/j.ces.2019.06.018 Smallbone, K., Malys, N., Messiha, H. L., Wishart, J. A., & Simeonidis, E. (2011). Building a kinetic model of trehalose biosynthesis in Saccharomyces cerevisiae. In Methods in Enzymology (1st ed., Vol. 500). Elsevier Inc. https://doi.org/10.1016/B978-0-12- 385118-5.00018-9 Smallbone, K., Messiha, H. L., Carroll, K. M., Winder, C. L., Malys, N., Dunn, W. B., Murabito, E., Swainston, N., Dada, J. O., Khan, F., Pir, P., Simeonidis, E., Spasić, I., Wishart, J., Weichart, D., Hayes, N. W., Jameson, D., Broomhead, D. S., Oliver, S. G., … Mendes, P. (2013). A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Letters, 587(17), 2832–2841. https://doi.org/10.1016/j.febslet.2013.06.043 Suarez-Mendez, C. a. (2015). Dynamics of Storage Carbohydrates Metabolism in Saccharomyces cerevisiae: A Quantitative (Issue december). Tanabe, S., Kobayashi, M., & Matsuda, K. (1987a). Yeast Glycogen Phosphorylase: Characterization of the Dimeric Form and Its Activation. Agricultural and Biological Chemistry, 51(9), 2465–2471. https://doi.org/10.1271/bbb1961.51.2465 Tanabe, S., Kobayashi, M., & Matsuda, K. (1987b). Yeast Glycogen Phosphorylase: Kinetic Properties Compared with Muscle and Potato Enzymes. Agricultural and Biological Chemistry, 52(3), 757–764. https://doi.org/10.1271/bbb1961.52.757 Tang, W., Deshmukh, A. T., Haringa, C., Wang, G., van Gulik, W., van Winden, W., Reuss, M., Heijnen, J. J., Xia, J., Chu, J., & Noorman, H. J. (2017). A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum. In Biotechnology and Bioengineering (Vol. 114, Issue 8). https://doi.org/10.1002/bit.26294 Teusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E., Van Der Weijden, C. C., Schepper, M., Walsh, M. C., Bakker, B. M., Van Dam, K., Westerhoff, H. V., & Snoep, J. L. (2000). Can yeast glycolysis be understood terms of vitro kinetics of the constituent enzymes? Testing biochemistry. European Journal of Biochemistry, 267(17), 5313–5329. https://doi.org/10.1046/j.1432-1327.2000.01527.x Traut, T. (2008). Regulatory Allosteric Enzymes. Springer US. Trevisol, E. T. V., Panek, A. D., De Mesquita, J. F., & Eleutherio, E. C. A. (2014). Regulation of the yeast trehalose-synthase complex by cyclic AMP-dependent phosphorylation. Biochimica et Biophysica Acta - General Subjects, 1840(6), 1646– 1650. https://doi.org/10.1016/j.bbagen.2013.12.010 Tripodi, F., Nicastro, R., Reghellin, V., & Coccetti, P. (2015). Post-translational modifications on yeast carbon metabolism: Regulatory mechanisms beyond transcriptional control. Biochimica et Biophysica Acta - General Subjects, 1850(4), 620–627. https://doi.org/10.1016/j.bbagen.2014.12.010 Turnquist, R., & Hansen, G. (1973). Uridine Diphosphoryl Glucose Pyrophosphorylase. 1, 51–71. Van’t Riet, K. (1991). Basic Bioreactor Design. Marcel Dekker, Inc. van Boekel, M. A. J. S., & Tijskens, L. M. M. (2001). Kinetic modelling. Food Process Modelling, December, 35–59. https://doi.org/10.1533/9781855736375.1.35 van Leer, B., & Powell, K. G. (2010). Introduction to Computational Fluid Dynamics. Encyclopedia of Aerospace Engineering, October. https://doi.org/10.1002/9780470686652.eae048 Vandercammen, A., François, J., & Hers, H. -G. (1989). Characterization of trehalose-6- phosphate synthase and trehalose-6-phosphate phosphatase of Saccharomyces cerevisiae. European Journal of Biochemistry, 182(3), 613–620. https://doi.org/10.1111/j.1432-1033.1989.tb14870.x Vasconcelos, J. M. T., Alves, S. S., & Barata, J. M. (1995). Mixing in gas-liquid contactors agitated by multiple turbines. Chemical Engineering Science, 50(14), 2343–2354. https://doi.org/10.1016/0009-2509(95)00090-R Vaseghi, S., Baumeister, A., Rizzi, M., & Reuss, M. (1999). In vivo dynamics of the pentose phosphate pathway in Saccharomyces cerevisiae. Metabolic Engineering, 1(2), 128–140. https://doi.org/10.1006/mben.1998.0110 von Stockar, U. (2013). Biothermodynamics: Role, The Engineering, Biochemical. EPFL Press. Vrábel, P., Van Der Lans, R. G. J. M., Cui, Y. Q., & Luyben, K. C. A. M. (1999). Compartment model approach: Mixing in large scale aerated reactors with multiple impellers. Chemical Engineering Research and Design, 77(4), 291–302. https://doi.org/10.1205/026387699526223 Vrábel, P., Van Der Lans, R. G. J. M., Luyben, K. C. A. M., Boon, L., & Nienow, A. W. (2000). Mixing in large-scale vessels stirred with multiple radial or radial and axial uppumping impellers: Modelling and measurements. Chemical Engineering Science, 55(23), 5881–5896. https://doi.org/10.1016/S0009-2509(00)00175-5 Vrábel, P., Van der Lans, R. G. J. M., Van der Schot, F. N., Luyben, K. C. A. M., Xu, B., & Enfors, S. O. (2001). CMA: Integration of fluid dynamics and microbial kinetics in modelling of large-scale fermentations. Chemical Engineering Journal, 84(3), 463– 474. https://doi.org/10.1016/S1385-8947(00)00271-0 Wilson, W. A., Boyer, M. P., Davis, K. D., Burke, M., & Roach, P. J. (2010). The subcellular localization of yeast glycogen synthase is dependent upon glycogen content. Canadian Journal of Microbiology, 56(5), 408–420. https://doi.org/10.1139/W10-027 Wilson, W. A., Roach, P. J., Montero, M., Baroja-Fernández, E., Muñoz, F. J., Eydallin, G., Viale, A. M., & Pozueta-Romero, J. (2010). Regulation of glycogen metabolism in yeast and bacteria. FEMS Microbiology Reviews, 34(6), 952–985. https://doi.org/10.1111/j.1574-6976.2010.00220.x Xia, J., Wang, G., Lin, J., Wang, Y., Chu, J., Zhuang, Y., & Zhang, S. (2015). Advances and Practices of Bioprocess Scale-up. Advances in Biochemical Engineering/Biotechnology, January 2015. https://doi.org/10.1007/10 Yavari, M., Ebrahimi, S., Aghazadeh, V., & Ghashghaee, M. (2019). Kinetics of different bioreactor systems with Acidithiobacillus ferrooxidans for ferrous iron oxidation. Reaction Kinetics, Mechanisms and Catalysis, 128(2), 611–627. https://doi.org/10.1007/s11144-019-01660-3 Zahradník, J., Mann, R., Fialová, M., Vlaev, D., Vlaev, S. D., Lossev, V., & Seichter, P. (2001). A networks-of-zones analysis of mixing and mass transfer in three industrial bioreactors. Chemical Engineering Science, 56(2), 485–492. https://doi.org/10.1016/S0009-2509(00)00252-9 Zhong, J. J. (2011). Bioreactor Engineering. In Comprehensive Biotechnology, Second Edition (Second Edi, Vol. 2). Elsevier B.V. https://doi.org/10.1016/B978-0-08- 088504-9.00097-0
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	166 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Ingeniería Química
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/84402/4/license.txt https://repositorio.unal.edu.co/bitstream/unal/84402/5/1036954383.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/84402/6/1036954383.2022.pdf.jpg
bitstream.checksum.fl_str_mv	eb34b1cf90b7e1103fc9dfd26be24b4a e3260ab0ce8fafc97a5b60424569112c f002cb8d04fc77952cf905ded1e4b850
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089487072886784
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Suárez Méndez, Camilo Alberto997598635d63c1552bb6d43d1091be66600Moreno Otálvaro, Sebastiáncd2e02e9455005b38e56f865cc087a23Bioprocesos y Flujos Reactivos2023-08-01T19:19:52Z2023-08-01T19:19:52Z2022-12-04https://repositorio.unal.edu.co/handle/unal/84402Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesModelar un bioproceso es crucial para tener un entendimiento y predecir variables de operación importantes en la industria. Para hacerlo no solo es pertinente modelar el biorreactor con sus fenómenos de transporte sino también algunos procesos intracelulares que transforman el sustrato disponible. De esta forma, el objetivo general de esta tesis es realizar un modelamiento multiescala que comprende la escala macro (reactor) y la escala micro (metabolismo celular). La descripción matemática del biorreactor consistió en un modelo de compartimentos, con el fin de calcular los gradientes de concentración. Para la configuración dada del reactor se calculó un tiempo de mezcla de 70s si la alimentación es en el medio del tanque. Por otro lado, se construyó un modelo cinético para predecir datos experimentales del metabolismo de carbono central de Saccharomyces cerevisiae; específicamente para la glucólisis, la ruta de las pentosas fosfato y el metabolismo de trehalosa y glucógeno. Se realizó la estimación de parámetros y se obtuvo diferentes respuestas. Por ejemplo, para la glucosa intracelular el error fue del 3.8% y para fructosa 6-fosfato del 1%. Además, el modelo derivado aquí para el pool de glucógeno funcionará como base para trabajos futuros, ya que su modelamiento no ha sido lo suficientemente explorado en la literatura. Después, se acoplaron ambos modelos a través de la tasa de consumo de sustrato �� . Se simuló un pulso de alimento y se construyeron mapas de calor que muestran los gradientes de concentración en el reactor para la glucosa extracelular, metabolitos intracelulares y �� . Por último, se hizo un análisis de sensibilidad según la velocidad de agitación y el número de pulsos. Se obtuvo que a una velocidad de 180 o 240 rpm se disminuyen en gran medida los gradientes. Con el segundo análisis, se concluyó que tanto la trehalosa como el glucógeno son pooles de carbono que funcionan como buffer frente a perturbaciones de concentración en el entorno del microorganismo. (texto tomado de la fuente)Modelling a bioprocess is key for general understanding and predicting important operation variables at industry level. To do so, it is not only relevant to model the bioreactor and the associated transport phenomena, but also to model intracellular processes in charge of converting the available substrate. In this direction, the general objective of this thesis is to perform multiscale modeling considering the macroscale (reactor) and the microscale (cellular metabolism). The mathematical description of the bioreactor consists of a compartment model to compute concentration gradients. For the chosen reactor configuration, a mixing time of 70s is estimated when the feeding point is located somewhere at the middle section of the vessel. On the other hand, a kinetic model is built to predict experimental data from central carbon metabolism of Saccharomyces cerevisiae; specifically, glycolysis, pentose phosphate pathway and the trehalose and glycogen metabolism. Parameter estimation is performed resulting in different responses. For instance, for intracellular glucose an error of approximately 3.8% is obtained while for fructose 6-phosphate the error is about 1%. In addition, the model derived here for the glycogen pool might serve as a basis for future work, because its modelling has not yet been explored enough in literature. Then, both models are coupled by the substrate consumption velocity �� . A feeding pulse is simulated, and heat maps are constructed showing concentration gradients throughout the reactor for the extracellular glucose, intracellular metabolites and �� . Finally, a sensitivity analysis is performed according to the agitation speed and the number of pulses. As a result, the observed concentration gradients are significantly reduced when a stirring speed of 180 or 240 rpm is used. From the second analysis, a notable remark is that both, trehalose, and glycogen carbon pools, seem to buffer the carbon flux against concentration perturbations in the microorganism environment.MaestríaMagíster en Ingeniería - Ingeniería QuímicaÁrea curricular de Ingeniería Química e Ingeniería de Petróleos166 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería QuímicaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaBioreactoresControl de procesos biotecnológicosModelamiento multiescalaModelo de compartimentosModelo cinéticoCarbohidratos de reservaMetabolismo de carbono centralSaccharomyces cerevisiaeMultiscale modelingCompartment modelKinetic modelStorage carbohydratesCentral carbon metabolismEvaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescalaEvaluation of the impact of concentration gradients on the microbial carbon central metabolism by a multiscale modelling approachTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAntoniewicz, M. R. (2021). A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications. Metabolic Engineering, 63(November 2020), 2–12. https://doi.org/10.1016/j.ymben.2020.11.002Ascanio, G. (2015). Mixing time in stirred vessels: A review of experimental techniques. Chinese Journal of Chemical Engineering, 23(7), 1065–1076. https://doi.org/10.1016/j.cjche.2014.10.022Baskaran, S. (2010). Structure and Regulation of Yeast Glycogen Synthase (Issue August) [Indiana University]. https://doi.org/10.1007/978-3-319-43589-3_1Bond, C. J., Jurica, M. S., Mesecar, A., & Stoddard, B. L. (2000). Determinants of allosteric activation of yeast pyruvate kinase and identification of novel effectors using computational screening. Biochemistry, 39(50), 15333–15343. https://doi.org/10.1021/bi001443iChan, C. Y., & Parra, K. J. (2014). Yeast phosphofructokinase-1 subunit Pfk2p is necessary for pH homeostasis and glucose-dependent vacuolar ATPase reassembly. Journal of Biological Chemistry, 289(28), 19448–19457. https://doi.org/10.1074/jbc.M114.569855Charpentier, J. C. (2009). Perspective on multiscale methodology for product design and engineering. Computers and Chemical Engineering, 33(5), 936–946. https://doi.org/10.1016/j.compchemeng.2008.11.007Cleland, W. W. (1963a). Biochimica Et Biophysica Acta the Kinetics of Enzyme-Catalyzed Re Ti With Two or More Substrates or Pr D Ct I. Nomen Clature a Td Rate Equatio. Biochirn. Biophys. Acta, 67(2), 67. https://doi.org/10.1016/0926-6569(63)90211-6Cleland, W. W. (1963b). The kinetics of enzyme-catalyzed reactions with two or more substrates or products. II. Inhibition: Nomenclature and theory. BBA - Biochimica et Biophysica Acta, 67(C), 173–187. https://doi.org/10.1016/0006-3002(63)91815-8Cui, Y. Q., Van der Lans, R. G. J. M., & Luyben, K. C. A. M. (1996). Local Power Uptake in Gas-Liquid Systems With Single and Multiple Rushton Turbines. 51(1), 2631– 2636.Doran, P. M. (2012). Bioprocess engineering principles: Second edition. In Bioprocess Engineering Principles: Second Edition (Vol. 9780080917).Flamholz, A. (2018). eQuilibrator. https://equilibrator.weizmann.ac.il/François, J., & Parrou, J. L. (2001). Reserve carbohydrates metabolism in the yeast Saccharomyces cerevisiae. FEMS Microbiology Reviews, 25(1), 125–145. https://doi.org/10.1016/S0168-6445(00)00059-0Gao, H., & Leary, J. A. (2003). Multiplex inhibitor screening and kinetic constant determinations for yeast hexokinase using mass spectrometry based assays. Journal of the American Society for Mass Spectrometry, 14(3), 173–181. https://doi.org/10.1016/S1044-0305(02)00867-XGeerlof, A., Travers, F., Barman, T., & Lionne, C. (2005). Perturbation of yeast 3- phosphoglycerate kinase reaction mixtures with ADP: Transient kinetics of formation of ATP from bound 1,3-bisphosphoglycerate. Biochemistry, 44(45), 14948–14955. https://doi.org/10.1021/bi0512290Gelves, R., Dietrich, A., & Takors, R. (2014). Modeling of gas-liquid mass transfer in a stirred tank bioreactor agitated by a Rushton turbine or a new pitched blade impeller. Bioprocess and Biosystems Engineering, 37(3), 365–375. https://doi.org/10.1007/s00449-013-1001-8Gikas, P., & Livingston, A. G. (1998). Use of specific ATP concentration and specific oxygen uptake rate to determine parameters of a structured model of biomass growth. Enzyme and Microbial Technology, 22(6), 500–510. https://doi.org/10.1016/S0141-0229(97)00242-1Hahn, J. (2020). From Parts to the Whole A Whole-Cell Model for Saccharomyces cerevisiae. Humboldt-university BerlinHajian, C. S. S., Haringa, C., Noorman, H., & Takors, R. (2020). Predicting by-product gradients of baker’s yeast production at industrial scale: A practical simulation approach. Processes, 8(12), 1–19. https://doi.org/10.3390/pr8121554Haringa, C., Tang, W., Deshmukh, A. T., Xia, J., Reuss, M., Heijnen, J. J., Mudde, R. F., & Noorman, H. J. (2016). Euler-Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines. Engineering in Life Sciences, 16(7), 652–663. https://doi.org/10.1002/elsc.201600061Haringa, C., Tang, W., Wang, G., Deshmukh, A. T., van Winden, W. A., Chu, J., van Gulik, W. M., Heijnen, J. J., Mudde, R. F., & Noorman, H. J. (2018). Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization. Chemical Engineering Science, 175, 12–24. https://doi.org/10.1016/j.ces.2017.09.020Hess, B., & Plesser, T. (1979). Temporal and Spatial Order in Biochemical Systems. Annals of the New York Academy of Sciences, 316(1), 203–213. https://doi.org/10.1111/j.1749-6632.1979.tb29470.xHori, K., & Unno, H. (2011). Integrated Production and Separation. In Comprehensive Biotechnology, Second Edition (Second Edi, Vol. 2). Elsevier B.V. https://doi.org/10.1016/B978-0-08-088504-9.00116-1Ingram, G. D., Cameron, I. T., & Hangos, K. M. (2004). Classification and analysis of integrating frameworks in multiscale modelling. Chemical Engineering Science, 59(11), 2171–2187. https://doi.org/10.1016/j.ces.2004.02.010Johnson, K. A., & Goody, R. S. (2011). The original Michaelis constant: Translation of the 1913 Michaelis-Menten Paper. Biochemistry, 50(39), 8264–8269. https://doi.org/10.1021/bi201284uJourdan, N., Neveux, T., Potier, O., Kanniche, M., Wicks, J., Nopens, I., Rehman, U., & Le Moullec, Y. (2019). Compartmental Modelling in chemical engineering: A critical review. Chemical Engineering Science, 210, 115196. https://doi.org/10.1016/j.ces.2019.115196Kesten, D., Kummer, U., Sahle, S., & Hübner, K. (2015). A new model for the aerobic metabolism of yeast allows the detailed analysis of the metabolic regulation during glucose pulse. Biophysical Chemistry, 206, 40–57. https://doi.org/10.1016/j.bpc.2015.06.010King, E. L., & Altman, C. (1956). A schematic method of deriving the rate laws for enzyme-catalyzed reactions. Journal of Physical Chemistry, 60(10), 1375–1378. https://doi.org/10.1021/j150544a010Klipp, E., Herwig, P., Kowald, A., Wierling, C., & Lehrach, H. (2005). Systems Biology in Practice.Lam, C. F., & Priest, D. G. (1972). Enzyme Kinetics: Systematic Generation of Valid KingAltman Patterns. Biophysical Journal, 12(3), 248–256. https://doi.org/10.1016/S0006-3495(72)86084-3Lapin, A., Schmid, J., & Reuss, M. (2006). Modeling the dynamics of E. coli populations in the three-dimensional turbulent field of a stirred-tank bioreactor-A structuredsegregated approach. Chemical Engineering Science, 61(14), 4783–4797. https://doi.org/10.1016/j.ces.2006.03.003Larsson, C., Påhlman, I. L., & Gustafsson, L. (2000). The importance of ATP as a regulator of glycolytic flux in Saccharomyces cerevisiae. Yeast, 16(9), 797–809. https://doi.org/10.1002/1097-0061(20000630)16:9<797::AID-YEA553>3.0.CO;2-5Larsson, M., & Arvidsson, L. (1971). Inhibition of Phosphoglycerate Kinase by Products and Product Homologues. 22, 506–512.Leskovac, V. (2004). COMPREHENSIVE ENZYME KINETICS. Kluwer Academic Publishers, 438. https://doi.org/10.1007/0-306-48390-4_5Luo, Y., Kurian, V., & Ogunnaike, B. A. (2021). Bioprocess systems analysis, modeling, estimation, and control. Current Opinion in Chemical Engineering, 33, 100705. https://doi.org/10.1016/j.coche.2021.100705Mayr, B., Horvat, P., Nagy, E., & Moser, A. (1993). Mixing-models applied to industrial batch bioreactors. Bioprocess Engineering, 9(1), 1–12. https://doi.org/10.1007/BF00389534Messiha, H., Kent, E., Malys, N., Carroll, K., Mendes, P., & Smallbone, K. (2014). Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast. PeerJ PrePrints, April. https://doi.org/10.7287/peerj.preprints.146v4Moisset, P., Vaisman, D., Cintolesi, A., Urrutia, J., Rapaport, I., Andrews, B. A., & Asenjo, J. A. (2012). Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters. Biotechnology and Bioengineering, 109(9), 2325–2339. https://doi.org/10.1002/bit.24503Monod, J., Wyman, J., & Changeux, J. P. (1965). On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology, 12(1), 88–118. https://doi.org/10.1016/S0022-2836(65)80285-6Morchain, J. (2017a). Bioreactor Modeling. In Bioreaction Engineering Principles. Elsevier. https://doi.org/10.1007/978-1-4757-4645-7_9Morchain, J. (2017b). Numerical Tools for Scaling Up Bioreactors. Current Developments in Biotechnology and Bioengineering: Bioprocesses, Bioreactors and Controls, 495– 523. https://doi.org/10.1016/B978-0-444-63663-8.00017-3Mulcahy, P., O’Flaherty, M., Jennings, L., & Griffin, T. (2002). Application of kinetic-based biospecific affinity chromatographic systems to ATP-dependent enzymes: Studies with yeast hexokinase. Analytical Biochemistry, 309(2), 279–292. https://doi.org/10.1016/S0003-2697(02)00307-XMuloiwa, M., Nyende-Byakika, S., & Dinka, M. (2020). Comparison of unstructured kinetic bacterial growth models. South African Journal of Chemical Engineering, 33(July), 141–150. https://doi.org/10.1016/j.sajce.2020.07.006Nadal-Rey, G., McClure, D. D., Kavanagh, J. M., Cornelissen, S., Fletcher, D. F., & Gernaey, K. V. (2021). Understanding gradients in industrial bioreactors. Biotechnology Advances, 46(October 2020), 107660. https://doi.org/10.1016/j.biotechadv.2020.107660Neet, K. E. (1995). Cooperativity in enzyme function: Equilibrium and kinetic aspects. Methods in Enzymology, 249(C), 519–567. https://doi.org/10.1016/0076- 6879(95)49048-5Nielsen, J. (2014). Bioreaction Engineering Principles. In Psychological Science (Vol. 25, Issue 9).Nienow, A. W. (1998). Hydrodynamics of stirred bioreactors. Applied Mechanics Reviews, 51(1), 3–32. https://doi.org/10.1115/1.3098990Noorman, H. J., & Heijnen, J. J. (2017). Biochemical engineering’s grand adventure. Chemical Engineering Science, 170, 677–693. https://doi.org/10.1016/j.ces.2016.12.065Oide, S., & Inui, M. (2017). Trehalose acts as a uridine 5′-diphosphoglucose-competitive inhibitor of trehalose 6-phosphate synthase in Corynebacterium glutamicum. FEBS Journal, 284(24), 4298–4313. https://doi.org/10.1111/febs.14309Papagianni, M. (2012). Recent advances in engineering the central carbon metabolism of industrially important bacteria. Microbial Cell Factories, 11, 1–13. https://doi.org/10.1186/1475-2859-11-50Pigou, M., & Morchain, J. (2015). Investigating the interactions between physical and biological heterogeneities in bioreactors using compartment, population balance and metabolic models. Chemical Engineering Science, 126(April), 267–282. https://doi.org/10.1016/j.ces.2014.11.035Purich, D. L. (2009). Contemporary Enzyme Kinetics and Mechanism: Reliable Lab Solutions. http://books.google.com/books?hl=en&lr=&id=Pr3V80HZLpkC&pgis=1Richter, O., Betz, A., & Giersch, C. (1975). The response of oscillating glycolysis to perturbations in the NADH/NAD system: A comparison between experiments and a computer model. BioSystems, 7(1), 137–146. https://doi.org/10.1016/0303- 2647(75)90051-9Rizzi, M., Baltes, M., Theobald, U., & Reuss, M. (1997). In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnology and Bioengineering, 55(4), 592–608. https://doi.org/10.1002/(SICI)1097- 0290(19970820)55:4<592::AID-BIT2>3.0.CO;2-CRothman, L. B., & Cabib, E. (1967a). Allosteric Properties of Yeast Glycogen Synthetase. I. General Kinetic Study. Biochemical and Biophysical Research Communications, 6(7), 644–650. https://doi.org/10.1016/0006-291X(66)90503-1Rothman, L. B., & Cabib, E. (1967b). Allosteric Properties of Yeast Glycogen Synthetase. II. The Effect of pH on Inhibition and Its Physiological Implications. Biochemistry, 6(7), 2107–2112.Rudolph, F. B., & Fromm, H. J. (1971). Computer simulation studies with yeast hexokinase and additional evidence for the random Bi Bi mechanism. Journal of Biological Chemistry, 246(21), 6611–6619. https://doi.org/10.1016/s0021- 9258(19)34158-4Saa, P. A., & Nielsen, L. K. (2017). Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnology Advances, 35(8), 981–1003. https://doi.org/10.1016/j.biotechadv.2017.09.005Siebler, F., Lapin, A., Hermann, M., & Takors, R. (2019). The impact of CO gradients on C. ljungdahlii in a 125 m3 bubble column: Mass transfer, circulation time and lifeline analysis. Chemical Engineering Science, 207, 410–423. https://doi.org/10.1016/j.ces.2019.06.018Smallbone, K., Malys, N., Messiha, H. L., Wishart, J. A., & Simeonidis, E. (2011). Building a kinetic model of trehalose biosynthesis in Saccharomyces cerevisiae. In Methods in Enzymology (1st ed., Vol. 500). Elsevier Inc. https://doi.org/10.1016/B978-0-12- 385118-5.00018-9Smallbone, K., Messiha, H. L., Carroll, K. M., Winder, C. L., Malys, N., Dunn, W. B., Murabito, E., Swainston, N., Dada, J. O., Khan, F., Pir, P., Simeonidis, E., Spasić, I., Wishart, J., Weichart, D., Hayes, N. W., Jameson, D., Broomhead, D. S., Oliver, S. G., … Mendes, P. (2013). A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Letters, 587(17), 2832–2841. https://doi.org/10.1016/j.febslet.2013.06.043Suarez-Mendez, C. a. (2015). Dynamics of Storage Carbohydrates Metabolism in Saccharomyces cerevisiae: A Quantitative (Issue december).Tanabe, S., Kobayashi, M., & Matsuda, K. (1987a). Yeast Glycogen Phosphorylase: Characterization of the Dimeric Form and Its Activation. Agricultural and Biological Chemistry, 51(9), 2465–2471. https://doi.org/10.1271/bbb1961.51.2465Tanabe, S., Kobayashi, M., & Matsuda, K. (1987b). Yeast Glycogen Phosphorylase: Kinetic Properties Compared with Muscle and Potato Enzymes. Agricultural and Biological Chemistry, 52(3), 757–764. https://doi.org/10.1271/bbb1961.52.757Tang, W., Deshmukh, A. T., Haringa, C., Wang, G., van Gulik, W., van Winden, W., Reuss, M., Heijnen, J. J., Xia, J., Chu, J., & Noorman, H. J. (2017). A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum. In Biotechnology and Bioengineering (Vol. 114, Issue 8). https://doi.org/10.1002/bit.26294Teusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E., Van Der Weijden, C. C., Schepper, M., Walsh, M. C., Bakker, B. M., Van Dam, K., Westerhoff, H. V., & Snoep, J. L. (2000). Can yeast glycolysis be understood terms of vitro kinetics of the constituent enzymes? Testing biochemistry. European Journal of Biochemistry, 267(17), 5313–5329. https://doi.org/10.1046/j.1432-1327.2000.01527.xTraut, T. (2008). Regulatory Allosteric Enzymes. Springer US.Trevisol, E. T. V., Panek, A. D., De Mesquita, J. F., & Eleutherio, E. C. A. (2014). Regulation of the yeast trehalose-synthase complex by cyclic AMP-dependent phosphorylation. Biochimica et Biophysica Acta - General Subjects, 1840(6), 1646– 1650. https://doi.org/10.1016/j.bbagen.2013.12.010Tripodi, F., Nicastro, R., Reghellin, V., & Coccetti, P. (2015). Post-translational modifications on yeast carbon metabolism: Regulatory mechanisms beyond transcriptional control. Biochimica et Biophysica Acta - General Subjects, 1850(4), 620–627. https://doi.org/10.1016/j.bbagen.2014.12.010Turnquist, R., & Hansen, G. (1973). Uridine Diphosphoryl Glucose Pyrophosphorylase. 1, 51–71.Van’t Riet, K. (1991). Basic Bioreactor Design. Marcel Dekker, Inc.van Boekel, M. A. J. S., & Tijskens, L. M. M. (2001). Kinetic modelling. Food Process Modelling, December, 35–59. https://doi.org/10.1533/9781855736375.1.35van Leer, B., & Powell, K. G. (2010). Introduction to Computational Fluid Dynamics. Encyclopedia of Aerospace Engineering, October. https://doi.org/10.1002/9780470686652.eae048Vandercammen, A., François, J., & Hers, H. -G. (1989). Characterization of trehalose-6- phosphate synthase and trehalose-6-phosphate phosphatase of Saccharomyces cerevisiae. European Journal of Biochemistry, 182(3), 613–620. https://doi.org/10.1111/j.1432-1033.1989.tb14870.xVasconcelos, J. M. T., Alves, S. S., & Barata, J. M. (1995). Mixing in gas-liquid contactors agitated by multiple turbines. Chemical Engineering Science, 50(14), 2343–2354. https://doi.org/10.1016/0009-2509(95)00090-RVaseghi, S., Baumeister, A., Rizzi, M., & Reuss, M. (1999). In vivo dynamics of the pentose phosphate pathway in Saccharomyces cerevisiae. Metabolic Engineering, 1(2), 128–140. https://doi.org/10.1006/mben.1998.0110von Stockar, U. (2013). Biothermodynamics: Role, The Engineering, Biochemical. EPFL Press.Vrábel, P., Van Der Lans, R. G. J. M., Cui, Y. Q., & Luyben, K. C. A. M. (1999). Compartment model approach: Mixing in large scale aerated reactors with multiple impellers. Chemical Engineering Research and Design, 77(4), 291–302. https://doi.org/10.1205/026387699526223Vrábel, P., Van Der Lans, R. G. J. M., Luyben, K. C. A. M., Boon, L., & Nienow, A. W. (2000). Mixing in large-scale vessels stirred with multiple radial or radial and axial uppumping impellers: Modelling and measurements. Chemical Engineering Science, 55(23), 5881–5896. https://doi.org/10.1016/S0009-2509(00)00175-5Vrábel, P., Van der Lans, R. G. J. M., Van der Schot, F. N., Luyben, K. C. A. M., Xu, B., & Enfors, S. O. (2001). CMA: Integration of fluid dynamics and microbial kinetics in modelling of large-scale fermentations. Chemical Engineering Journal, 84(3), 463– 474. https://doi.org/10.1016/S1385-8947(00)00271-0Wilson, W. A., Boyer, M. P., Davis, K. D., Burke, M., & Roach, P. J. (2010). The subcellular localization of yeast glycogen synthase is dependent upon glycogen content. Canadian Journal of Microbiology, 56(5), 408–420. https://doi.org/10.1139/W10-027Wilson, W. A., Roach, P. J., Montero, M., Baroja-Fernández, E., Muñoz, F. J., Eydallin, G., Viale, A. M., & Pozueta-Romero, J. (2010). Regulation of glycogen metabolism in yeast and bacteria. FEMS Microbiology Reviews, 34(6), 952–985. https://doi.org/10.1111/j.1574-6976.2010.00220.xXia, J., Wang, G., Lin, J., Wang, Y., Chu, J., Zhuang, Y., & Zhang, S. (2015). Advances and Practices of Bioprocess Scale-up. Advances in Biochemical Engineering/Biotechnology, January 2015. https://doi.org/10.1007/10Yavari, M., Ebrahimi, S., Aghazadeh, V., & Ghashghaee, M. (2019). Kinetics of different bioreactor systems with Acidithiobacillus ferrooxidans for ferrous iron oxidation. Reaction Kinetics, Mechanisms and Catalysis, 128(2), 611–627. https://doi.org/10.1007/s11144-019-01660-3Zahradník, J., Mann, R., Fialová, M., Vlaev, D., Vlaev, S. D., Lossev, V., & Seichter, P. (2001). A networks-of-zones analysis of mixing and mass transfer in three industrial bioreactors. Chemical Engineering Science, 56(2), 485–492. https://doi.org/10.1016/S0009-2509(00)00252-9Zhong, J. J. (2011). Bioreactor Engineering. In Comprehensive Biotechnology, Second Edition (Second Edi, Vol. 2). Elsevier B.V. https://doi.org/10.1016/B978-0-08- 088504-9.00097-0EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84402/4/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD54ORIGINAL1036954383.2022.pdf1036954383.2022.pdfTesis de Maestría en Ingeniería - Ingeniería Químicaapplication/pdf6000960https://repositorio.unal.edu.co/bitstream/unal/84402/5/1036954383.2022.pdfe3260ab0ce8fafc97a5b60424569112cMD55THUMBNAIL1036954383.2022.pdf.jpg1036954383.2022.pdf.jpgGenerated Thumbnailimage/jpeg5053https://repositorio.unal.edu.co/bitstream/unal/84402/6/1036954383.2022.pdf.jpgf002cb8d04fc77952cf905ded1e4b850MD56unal/84402oai:repositorio.unal.edu.co:unal/844022024-08-17 00:01:02.603Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Evaluación del impacto de gradientes de concentración sobre el metabolismo del carbono central microbiano mediante un enfoque de modelamiento multiescala

Publicaciones similares