Heurística y simulación computacional para el diseño de un sistema de cultivo celular
Barra lateral del artículo
Contenido principal del artículo
Resumen
Un sistema fermentativo en etapa de desarrollo tecnológico va dirigido a la producción del ingrediente farmacéuticamente activo (IFA) de una vacuna contra la Peste Porcina Clásica empleando la línea celular HEK293. El objetivo fue proponer una estrategia de optimización aplicada al diseño del sistema de cultivo celular empleando la heurística sobre la base de la analogía entre el sistema experimental real y la simulación computacional. Para realizar las simulaciones del proceso se seleccionó el modelo cinético (Kontoravdi et al., 2007). Se mostró que los sistemas de fermentación reales han sido operados en condiciones muy alejadas de las óptimas y que el comportamiento de estos responde al errado régimen de alimentación al biorreactor que provoca la acumulación de productos tóxicos del metabolismo y el apagado del biorreactor. A partir de estos resultados se propone una estrategia de optimización que tiene en cuenta las características específicas del sistema de cultivo celular, la complejidad del modelo cinético seleccionado y que aprovecha las prestaciones del software MATLAB.
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Citas
Almquist, J., Cvijovic, M., Hatzimanikatis, V., & Nielsen, J. (2014). Kinetic models in industrial biotechnology – Improving cell factory performance. Metabolic Engineering, 24, 38-60. https://doi.org/10.1016/j.ymben.2014.03.007.
Baughman, A., Sharfstein, S., & Martin, L. (2011). A flexible state-space approach for the modeling of metabolic networks II: Advanced interrogation of hybridoma metabolism. Metabolic Engineering, 13, 138-149. doi: 10.1016/j.ymben.2010.12.003.
Bibila, T., & Flickinge, M. (1992). Use of a structured kinetic model of antibody synthesis and secretion for optimization of antibody production systems: I. Steadystate analysis. Biotechnology and Bioengineering, 39, 251-doi: 10.1002/bit.260390302.
Chen, N., & Tong, C. (2010). Antigenic analysis of classical swine fever virus E2 glycoprotein using pig antibodies identifies residues contributing to antigenic variation of the vaccine C-strain and group 2 strains circulating in China. Virology Journal, 7, 378. doi: 10.1186/1743-422X-7-378.
Cocinero, P. (2015). Método heurístico y su incidencia en el aprendizaje del álgebra. (Tesis de Licenciatura, Universidad Rafael Landívar]. https://xdoc.mx/preview/metodo-heuristico-y-su-incidencia-en-el-aprendizaje-5ee7dd61bc31b.
Dewasme, L., Côte, F., Filee, P., Hantson, A., & Vande, A. (2017). Macroscopic dynamic modeling of sequential batch cultures of hybridoma cells: An experimental validation. Bioengineering, 4, 1-20. doi: 10.3390/bioengineering4010017.
Dunn, M., & Burghes, A. (1983). High resolution two-dimensional polyacrylamide gel electrophoresis. Electrophoresis, 4, 97-116. doi: 10.1002/elps.1150040202.
Fenge, C., & Lüllau, E. (2006). Cell culture bioreactors. In S. S. Ozturk & W.S. Hu (Eds.), Cell culture technology for pharmaceutical and cell based therapies United States of America: Taylor & Francis Group.
Figueiredo, P., & Loiola, E. (2017). The impact of project introduction heuristics on research and development performance. Revista de Administración e innovación, 14, 151–161. https://doi.org/10.1016/j.rai.2017.03.004.
Frías, M., & Percedo, M. (2003). Reconociendo la Peste Porcina Clásica. Roma, Italia. https://catalogosiidca.csuca.org/Record/UNANI.054560/Details.
Gagnon, M., Hiller, G., Luan, Y., Kittredge, A., DeFelice, Y., & Drapeau, D. (2011). High-end pH-controlled delivery of glucose effectively suppresses lactate accumulation in cho fed-batch cultures. Biotechnology and Bioengineering, 108(6), 1328-1337. doi: 10.1002/bit.23072.
Gernaey, K., & Gani, R. (2010). A model-based systems approach to pharmaceutical product-process design and analysis. Chemical Engineering Science, 65, 5757-5769. https://doi.org/10.1016/j.ces.2010.05.003.
Ghorbaniaghdam, A. (2013). Development of a dynamic model to describe cho cells metabolic network and regulation. [Tesis Doctoral, Universidad de Montreal]. https://publications.polymtl.ca/1228/1/2013_AtefehGhorbaniaghdam.pdf.
Gómez, J., García, I., Nuñes, A., Salguero, F., Ruiz, E., Romero, J. (2006). Neuropathologic study of experimental classical swine fever. Veterinary Pathology, 530-540. doi: 10.1354/vp.43-4-530.
González, E. (1991). Utilización del Análisis de Procesos en la intensificación de la producción de distintas industrias de Cuba. Universidad Central de las Villas Martha Abreu.
Ho, Y., Kiparissides, A., Pistikopoulos, E. N., & Mantalaris, A. (2012). Computational approach for understanding and improving GS-NS0 antibody production under hyperosmotic conditions. Journal of Bioscience and Bioengineering, 113, 88 - 98. doi: 10.1016/j.jbiosc.2011.08.022.
Jang, J., & Barford, J. (2000). An unstructured kinetic model of macromolecular metabolism in batch and fed-batch cultures of hybridoma cells producing monoclonal antibody. Biochemical Engineering Journal, 4(2), 153–168. https://doi.org/10.1016/S1369-703X(99)00041-8.
Kiparissides, A., Koutinas, M., Kontoravdi, C., Mantalaris, A., & Pistikopoulos, E. (2011). Closing the loop’ in biological systems modeling - From the in silico to the in vitro. Automatica, 5, 9. doi: 10.1016/j.automatica.2011.01.013.
Komolpis, K., Udomchokmongkol, C., Phutong, S., & Palaga, T. (2010). Comparative production of a monoclonal antibody specific for enrofloxacin in a stirred-tank bioreactor. Journal of Industrial and Engineering Chemistry, 16(4), 567-571. https://doi.org/10.1016/j.jiec.2010.03.018.
Kontoravdi, C. (2006). An integrated modelling/ experimental framework for protein-producing cell cultures. [Tesis Doctoral, Imperial College London]. https://spiral.imperial.ac.uk/bitstream/10044/1/11796/2/Kontoravdi-C2007-PhD-Thesis.pdf.
Kontoravdi, C., Asprey, S., Pistikopoulos, E., & Mantalaris, A. (2007). Development of a dynamic model of monoclonal antibody production and glycosylation for product quality monitoring. Computers and Chemical Engineering, 31, 392–400. https://doi.org/10.1016/j.compchemeng.2006.04.009.
Kontoravdi, C., Pistikopoulos, E., & Mantalaris, A. (2010). Systematic development of predictive mathematical models for animal cell cultures. Computers and Chemical Engineering, 34, 1192-1198. https://doi.org/10.1016/j.compchemeng.2010.03.012.
Kwang, T., & Lee, G. (2015). Glutamine substitution: the role it can play to enhance therapeutic protein production. Pharmaceutical Bioprocess, 3(3), 249–261. https://www.openaccessjournals.com/articles/glutaminesubstitution-the-role-it-can-play-to-enhancetherapeutic-protein-production.pdf.
Li, X., Wang, L., Zhao, D., Zhang, G., Luo, J., Deng, R., & Yeng, Y. (2011). Identification of host cell binding peptide from an overlapping peptide library for inhibition of classical swine fever virus infection. Virus Genes, 43(1), 33-40. doi: 10.1007/s11262-011-0595-7.
Liu, Y., Bi, J., Zeng, A., & Yuan, J. (2008). A simple kinetic model for myeloma cell culture with consideration of lysine limitation. Bioprocess and Biosystems Engineering, 31(6), 569-577. doi: 10.1007/s00449-008-0204-x.
Lohmeier, A., Thüte, T., Northoff, S., Hou, J., Munro, T., & Noll, T. (2013). Effects of perfusion processes under limiting conditions on different Chinese Hamster Ovary cells. Paper presented at the 23rd European Society for Animal Cell Technology (ESACT) Meeting: Better Cells for Better Health Lille, Francia.
Nfor, B., Verhaert, P., Wielen, L., Hubbuch, J., & Ottens, M. (2009). Rational and systematic protein purification process development: the next generation. Trends in Biotechnology, 27(12), 673-679. doi: 10.1016/j.tibtech.2009.09.002.
OECD/FAO. (2021). OCDE FAO Perspectivas Agrícolas 2021-2030. Paris: OECD Publishing.
Ozturk, S. (2006). Cell Culture Technology - An Overview. In S. S. Ozturk & W.-S. Hu (Eds.). United States of America: Taylor & Francis Group.
Papathanasiou, M., Quiroga, A., Oberdieck, R., Mantalaris, A., & Pistikopoulos, E. (2016). Development of advanced computational tools for the intensification of monoclonal antibody production. Computer Aided Process Engineering, 38, 1659-1664. https://doi.org/10.1016/B978-0-444-63428-3.50281.
Robitaille, J., Chen, J., & Jolicoeur, M. (2015). A single dynamic metabolic model can describe mab producing cho cell batch and fed-batch cultures on different culture media. PLOS ONE, 1-23. doi: 10.1371/journal.pone.0136815.
Sbarciog, M., Coutinho, D., & Vande, A. (2014). A simple output-feedback strategy for the control of perfused mammalian cell cultures. Control Engineering Practice, 32, 123-135. https://doi.org/10.1016/j.conengprac.2014.08.002.
Scott, C. (2004). Animal Cell Culture: HighMaintenance, but Worth the Trouble. BioProcess International.
Teixeira, A., Cunha, A., Clemente, J., Moreira, J., Cruz, H. J., Alves, P., Carrondo, M., & Oliveira, R. (2005). Modelling and optimization of a recombinant BHK-21 cultivation process using hybrid grey-box systems. Journal of Biotechnology, 118, 290–303. doi: 10.1016/j.jbiotec.2005.04.024.
The MathWorks Inc. (2020). Matlab (Version 9.8.0.1323502 (R2020a)) [Programa de computador]. Natick, MA, USA: The MathWorks Inc.
Umaña, P., & Bailey, J. (1997). A mathematical model of N-linked glycoform biosynthesis. Biotechnology Bioengineering, 55(6), 890-908. doi: 10.1002/(SICI)1097-0290(19970920)55:6<890::AID-BIT7>3.0.CO;2-B.
Wang, Y., Chu, J., Zhuang, Y., Wang, Y., Xia, J., & Zhang, S. (2009). Industrial bioprocess control and optimization in the context of systems biotechnology. Biotechnology Advances, 27, 989–995. doi: 10.1016/j.biotechadv.2009.05.022.
Weber, W., Link, N., & Fussenegger, M. (2006). A genetic redox sensor for mammalian cells. Metabolic Engineering 8, 273-280. doi: 10.1016/j.ymben.2005.12.004.
Zadeh, K. (2011). A synergic simulation-optimization approach for analyzing biomolecular dynamics in living organisms. Computers in Biology and Medicine, 41, 24-36. https://doi.org/10.1016/j.compbiomed.2010.11.002.