dc.contributorMatallana Pérez, Luis Gerónimo
dc.creatorSánchez Rendón, Julio César
dc.date2021-05-19T20:55:05Z
dc.date2021-05-19T20:55:05Z
dc.date2021-05-17
dc.date.accessioned2023-09-06T18:25:24Z
dc.date.available2023-09-06T18:25:24Z
dc.identifierhttps://repositorio.ucaldas.edu.co/handle/ucaldas/16620
dc.identifierUniversidad de Caldas
dc.identifierRepositorio institucional Universidad de Caldas
dc.identifierhttps://repositorio.ucaldas.edu.co
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8697098
dc.descriptionIlustraciones
dc.descriptionspa: El ácido itacónico es una molécula orgánica con un mercado de 40,000 toneladas anuales y amplia variedad de usos industriales como aditivo de detergentes, productos farmacéuticos, herbicidas, removedor de resinas, compuestos dentales, entre otros. Esta sustancia se produce generalmente por fermentación del hongo filamentoso Aspergillus terreus, sin embargo, no se han propuesto modelos matemáticos detallados del comportamiento dinámico de los metabolitos involucrados en su producción. El presente trabajo muestra el desarrollo de un modelo matemático basado en red metabólica para la producción de ácido itacónico, con diferenciación entre compartimientos celulares y la incorporación del antiporte oxalacetato/cis-aconitato presente en la mitocondria. Los parámetros de este modelo matemático fueron calculados a partir del optimizador enjambre de partículas (Global optimization toolbox) y el integrador “ode15s” ambos presentes en Matlab® R2018, función objetivo de mínimos cuadrados y los datos experimentales obtenidos por Pretruccioli y Rychtera & Wase. El modelo presenta un ajuste considerable (R2 = 0.6819) para el mejor conjunto de datos. Así mismo, el modelo propuesto es capaz de describir la dinámica del proceso de fermentación de ácido itacónico a nivel de citosol y mitocondria, el cual no ha sido previamente reportado.
dc.descriptioneng: Itaconic acid is an organic molecule with a market of 40,000 tons per year and a wide variety of industrial uses such as detergent additive, pharmaceuticals, herbicides, resin remover, dental compounds, among others. This substance is generally produced by fermentation of the filamentous fungus Aspergillus terreus; however, detailed mathematical models of the dynamic behavior of the metabolites involved in its production have not been proposed. The present work shows the development of a mathematical model based on a metabolic network for itaconic acid production, with differentiation between cellular compartments and the incorporation of the oxaloacetate/cis-aconitate antiport present in the mitochondria. The parameters of this mathematical model were calculated with particle swarm optimizer (Global optimization toolbox) and the integrator "ode15s" both present in Matlab® R2018, least squares objective function and the experimental data obtained by Pretruccioli and Rychtera & Wase. The model presents a considerable fit (R2 = 0.6819) for the best data set. Likewise, the proposed model is able to describe the dynamics of the itaconic acid fermentation process at the cytosol and mitochondrial level, which has not been previously reported.
dc.descriptionIntroducción / Materiales y métodos / Producción bioquímica de ácido itacónico / Comportamiento cualitativo de la fermentación de ácido itacónico / Estructuración del modelo matemático / Metabolismo involucrado en la producción de ácido itacónico / Ecuaciones de transporte / Reacciones bioquímicas / Modelo dinámico de producción de ácido itacónico / Algoritmo de optimización / Problema de estimación de parámetros / Resultados / Discusión Conclusiones y recomendaciones / Conclusiones / Recomendaciones / Referencias bibliográficas
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dc.languageeng
dc.languagespa
dc.publisherManizales
dc.relationAbt, V., Barz, T., Cruz-Bournazou, M. N., Herwig, C., Kroll, P., Möller, J., ... & Schenkendorf, R. (2018). Model-based tools for optimal experiments in bioprocess engineering. Current opinion in chemical engineering, 22, 244-252.
dc.relationBaburina, I., Dikdan, G., Guo, F., Tous, G. I., Root, B., & Jordan, F. (1998). Reactivity at the substrate activation site of yeast pyruvate decarboxylase: inhibition by distortion of domain interactions. Biochemistry, 37(5), 1245-1255
dc.relationBakken, L. R., & Olsen, R. A. (1983). Buoyant densities and dry-matter contents of microorganisms: conversion of a measured biovolume into biomass. Appl. Environ. Microbiol., 45(4), 1188-1195.
dc.relationBaup, S. (1836). Ueber eine neue Pyrogen-Citronensäure, und über Benennung der Pyrogen-Säuren überhaupt. Annalen der Pharmacie, 19(1), 29-38.
dc.relationBecker, S., & Kuznetsov, A. (Eds.). (2013). Transport in biological media. Newnes. Bender, T., Pena, G., & Martinou, J. C. (2015). Regulation of mitochondrial pyruvate uptake by alternative pyruvate carrier complexes. The EMBO journal, 34(7), 911-924
dc.relationBentley, R., & Thiessen, C. P. (1957). Biosynthesis of itaconic acid in Aspergillus terreus I. Tracer studies with C14-labeled substrates. Journal of Biological Chemistry, 226(2), 673- 687.
dc.relationBesserer, A., Puech-Pagès, V., Kiefer, P., Gomez-Roldan, V., Jauneau, A., Roy, S., ... & Séjalon-Delmas, N. (2006). Strigolactones stimulate arbuscular mycorrhizal fungi by activating mitochondria. PLoS biology, 4(7)
dc.relationCastro, M. A., Angulo, C., Brauchi, S., Nualart, F., & Concha, I. I. (2008). Ascorbic acid participates in a general mechanism for concerted glucose transport inhibition and lactate transport stimulation. Pflügers Archiv-European Journal of Physiology, 457(2), 519-528.
dc.relationConstantinou, A., & Polizzi, K. M. (2013). Opportunities for bioprocess monitoring using FRET biosensors. Biochemical Society Transactions, 41(5), 1146-1151.
dc.relationDe Carvalho, J. C., Magalhães, A. I., & Soccol, C. R. (2018). Biobased itaconic acid market and research trends—Is it really a promising chemical. Chim Oggi/Chem Today, 36, 56-8.
dc.relationDelvigne, F., Zune, Q., Lara, A. R., Al-Soud, W., & Sørensen, S. J. (2014). Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity. Trends in Biotechnology, 32(12), 608-616.
dc.relationDos Reis, T. F., Menino, J. F., Bom, V. L. P., Brown, N. A., Colabardini, A. C., Savoldi, M. & Goldman, G. H. (2013). Identification of glucose transporters in Aspergillus nidulans. PLoS One, 8(11).
dc.relationEndeward, V., Arias-Hidalgo, M., Al-Samir, S., & Gros, G. (2017). CO2 permeability of biological membranes and role of CO2 channels. Membranes, 7(4), 61.
dc.relationFernandes, R. L., Bodla, V. K., Carlquist, M., Heins, A. L., Lantz, A. E., Sin, G., & Gernaey, K. V. (2012). Applying mechanistic models in bioprocess development. Measurement, Monitoring, Modelling and Control of Bioprocesses, 137-166.
dc.relationFiebig, D. G., McAleer, M., & Bartels, R. (1992). Properties of ordinary least squares estimators in regression models with nonspherical disturbances. Journal of Econometrics, 54(1-3), 321-334
dc.relationKargi, F. (2009). Re-interpretation of the logistic equation for batch microbial growth in relation to Monod kinetics. Letters in applied microbiology, 48(4), 398-401.
dc.relationKinoshita, K. (1932). Über die Produktion von Itaconsäure und Mannit durch einen neuen Schimmelpilz Aspergillus itaconicus. Acta Phytochim, 5, 271-287.
dc.relationLi, A., van Luijk, N., ter Beek, M., Caspers, M., Punt, P., & van der Werf, M. (2011). A clonebased transcriptomics approach for the identification of genes relevant for itaconic acid production in Aspergillus. Fungal genetics and Biology, 48(6), 602-611.
dc.relationLin, W., Lian, Z., Gu, X., & Jiao, B. (2014). A local and global search combined particle swarm optimization algorithm and its convergence analysis. Mathematical Problems in Engineering, 2014.
dc.relationLiu, D., Xiao, Y., Evans, B. S., & Zhang, F. (2015). Negative feedback regulation of fatty acid production based on a malonyl-CoA sensor–actuator. ACS synthetic biology, 4(2), 132-140.
dc.relationManteifel, V. M., & Karu, T. I. (2005). Structure of mitochondria and activity of their respiratory chain in successive generations of yeast cells exposed to He-Ne laser light. Biology Bulletin, 32(6), 556-566.
dc.relationMcCommis, K. S., & Finck, B. N. (2015). Mitochondrial pyruvate transport: a historical perspective and future research directions. Biochemical journal, 466(3), 443-454.
dc.relationOkabe, M., Lies, D., Kanamasa, S., & Park, E. Y. (2009). Biotechnological production of itaconic acid and its biosynthesis in Aspergillus terreus. Applied microbiology and biotechnology, 84(4), 597-606.
dc.relationPetruccioli, M., Pulci, V., & Federici, F. (1999). Itaconic acid production by Aspergillus terreus on raw starchy materials. Letters in applied microbiology, 28(4), 309-312.
dc.relationPinto, M. A., & Immanuel, C. D. (2006). Parameter Identification for Cybernetic Models of Bioprocesses. Proceedings:AICHE 2006 Annual Meeting.
dc.relationPostawa, K., Szczygieł, J., & Kułażyński, M. (2020). A comprehensive comparison of ODE solvers for biochemical problems. Renewable Energy.
dc.relationRychtera, M., & Wase, D. J. (1981). The growth of Aspergillus terreus and the production of itaconic acid in batch and continuous cultures. The influence of pH. Journal of Chemical Technology and Biotechnology, 31(1), 509-521.
dc.relationSadino-Riquelme, M. C., Rivas, J., Jeison, D., Hayes, R. E., & Donoso-Bravo, A. (2020). Making sense of parameter estimation and model simulation in bioprocesses. Biotechnology and bioengineering, 117(5), 1357-1366.
dc.relationSchaefer, U., Boos, W., Takors, R., & Weuster-Botz, D. (1999). Automated sampling device for monitoring intracellular metabolite dynamics. Analytical biochemistry, 270(1), 88-96. Shampine, L. F., & Reichelt, M. W. (1997). The matlab ode suite. SIAM journal on scientific computing, 18(1), 1-22.
dc.relationSplittgerber, A. G. (1983). Simplified treatment of two-substrate enzyme kinetics. Journal of Chemical Education, 60(8), 651.
dc.relationTang, C., Sun, P., Yang, J., Huang, Y., & Wu, Y. (2019). Kinetics simulation of Cu and Cd removal and the microbial community adaptation in a periphytic biofilm reactor. Bioresource technology, 276, 199-203.
dc.relationTsoularis, A., & Wallace, J. (2002). Analysis of logistic growth models. Mathematical biosciences, 179(1), 21-55.
dc.relationvan der Straat, L., & de Graaff, L. H. (2014). Pathway transfer in fungi: transporters are the key to success. Bioengineered, 5(5), 335-339.
dc.relationVanrolleghem, P. A., & Dochain, D. (1998). Bioprocess model identification. In Advanced instrumentation, data interpretation, and control of biotechnological processes (pp. 251- 318). Springer, Dordrecht.
dc.relationWeuster-Botz, D., & De Graaf, A. A. (1996). Reaction engineering methods to study intracellular metabolite concentrations. Metabolic Engineering, 75-108.
dc.relationWierckx, N., Agrimi, G., Lübeck, P. S., Steiger, M. G., Mira, N. P., & Punt, P. J. (2020). Metabolic specialization in itaconic acid production: a tale of two fungi. Current opinion in biotechnology, 62, 153-159.
dc.relationWillke, T., & Vorlop, K. D. (2001). Biotechnological production of itaconic acid. Applied microbiology and biotechnology, 56(3-4), 289-295.).
dc.relationXia, X. (2012). Particle swarm optimization method based on chaotic local search and roulette wheel mechanism. Physics Procedia, 24, 269-275.
dc.relationXu, P. (2019). Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture. Biotechnology and bioengineering.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectácido itacónico
dc.subjectmodelo matemático
dc.subjectestimación de parámetros
dc.subjectitaconic acid
dc.subjectmathematical model
dc.subjectparameter estimation
dc.subjectBiología molecular
dc.subjectBioquímica
dc.titleEstimación de parámetros en modelo biomatemáticos: producción de ácido itacónico
dc.typeSimulación
dc.typeTrabajo de grado - Pregrado
dc.typehttp://purl.org/coar/resource_type/c_7a1f
dc.typeText
dc.typeinfo:eu-repo/semantics/bachelorThesis
dc.typeinfo:eu-repo/semantics/publishedVersion


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