Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D

The following document presents the development of an agent based model which simulates the behavior of mesenchymal stem cells (MSC), cardiomyocites and endothelial cells in a 3D-printed biodevice which is implanted on the infarcted myocardium as part of a potential treatment for the regeneration of...

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Autores:
Ramírez López, Diana Victoria
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2018
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
spa
OAI Identifier:
oai:red.uao.edu.co:10614/10497
Acceso en línea:
http://hdl.handle.net/10614/10497
Palabra clave:
Ingeniería Mecatrónica
Inteligencia artificial
Bioingeniería
Infarto cardíaco
Impresión en tercera dimensión
Células madre mesenquimales
Biodispositivo
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openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
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oai_identifier_str oai:red.uao.edu.co:10614/10497
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dc.title.spa.fl_str_mv Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
title Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
spellingShingle Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
Ingeniería Mecatrónica
Inteligencia artificial
Bioingeniería
Infarto cardíaco
Impresión en tercera dimensión
Células madre mesenquimales
Biodispositivo
title_short Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
title_full Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
title_fullStr Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
title_full_unstemmed Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
title_sort Modelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3D
dc.creator.fl_str_mv Ramírez López, Diana Victoria
dc.contributor.advisor.none.fl_str_mv Rojas Arciniegas, Álvaro José
dc.contributor.author.spa.fl_str_mv Ramírez López, Diana Victoria
dc.subject.spa.fl_str_mv Ingeniería Mecatrónica
Inteligencia artificial
Bioingeniería
Infarto cardíaco
Impresión en tercera dimensión
Células madre mesenquimales
Biodispositivo
topic Ingeniería Mecatrónica
Inteligencia artificial
Bioingeniería
Infarto cardíaco
Impresión en tercera dimensión
Células madre mesenquimales
Biodispositivo
description The following document presents the development of an agent based model which simulates the behavior of mesenchymal stem cells (MSC), cardiomyocites and endothelial cells in a 3D-printed biodevice which is implanted on the infarcted myocardium as part of a potential treatment for the regeneration of the affected tissue. Examples of computational modeling applied to the behavior of cells are shown and some 3D bioprinting techniques are explained, such as microextrusion, which is used for the fabrication of the biodevices. The context for which the model has been thought is also described, taking into account the use of 3D printing as fabrication technique and the in vitro tests that would validate the results. After this, some machine learning techniques are presented, given that they were considered as alternatives to develop the model. Following the implementation of an ordinary differential equation-based model, the use of agent based modeling was considered as a tool that would better allow including the cellular microenvironment characteristics in the model. Thus, the development of the model with the software Netlogo, its functioning and the result’s visualization are explained step by step. At last, some results are shown, which were obtained after running determined experiments defined through an experimental design and their analysis, which shows that the model can simulate processes that occur in the cellular microenvironment of the infarcted myocardium through the interactions of cells and that it allows the observation of emergent behaviors that can be helpful to determine the characteristics that favor the success that is expected the treatment with the 3D- printed biodevice.t
publishDate 2018
dc.date.accessioned.spa.fl_str_mv 2018-12-04T14:14:39Z
dc.date.available.spa.fl_str_mv 2018-12-04T14:14:39Z
dc.date.issued.spa.fl_str_mv 2018-09-12
dc.type.spa.fl_str_mv Trabajo de grado - Pregrado
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dc.rights.spa.fl_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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dc.rights.creativecommons.spa.fl_str_mv Atribución 4.0 Internacional (CC BY 4.0)
rights_invalid_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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Atribución 4.0 Internacional (CC BY 4.0)
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dc.format.spa.fl_str_mv application/pdf
dc.format.extent.spa.fl_str_mv 98 páginas
dc.coverage.spatial.spa.fl_str_mv Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí
dc.publisher.spa.fl_str_mv Universidad Autónoma de Occidente
dc.publisher.program.spa.fl_str_mv Ingeniería Mecatrónica
dc.publisher.department.spa.fl_str_mv Departamento de Automática y Electrónica
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
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dc.source.bibliographiccitation.spa.fl_str_mv 3D Printering: XT-CF20 Carbon Fiber Filament Review | Hackaday. (s.f.). Recuperado de https://hackaday.com/2016/09/07/3d-printering-xt-cf20-carbon- fiber-filament-review/ Álvarez, A. (2013). Modelización. computación y matemáticas contra el cáncer. Bastidas, O. (s.f.). Technical Note -Neubauer Chamber Cell Counting. Recuperado de www.celeromics.com Biba, M., Xhafa, F., Esposito, F., y Ferilli, S. (2011). Using Machine Learning Techniques for Modelling and Simulation of Metabolic Networks. International Conference on Complex, Intelligent, and Software Intensive Systems, 85–92. https://doi.org/10.1109/CISIS.2011.22 Briers, D., Haghighi, I., White, D., Kemp, M. L., y Belta, C. (2016). Pattern synthesis in a 3D agent-based model of stem cell differentiation. In 2016 IEEE 55th Conference on Decision and Control (CDC). Recuperado de https://doi.org/10.1109/CDC.2016.7798907 Cano, G., García-Rodríguez, J., Orts, S., García-García, A., Peña-García, J., Pérez- Garrido, A., y Pérez-Sánchez, H. (2017). Predicción de solubilidad de fármacos usando máquinas de soporte vectorial sobre unidades de procesamiento gráfico. Revista Internacional de Metodos Numericos Para Calculo Y Diseno En Ingenieria. Recuperado de https://doi.org/10.1016/j.rimni.2015.12.001 Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., y Jensen, K. F. (2017). Prediction of Organic Reaction Outcomes Using Machine Learning. ACS Central Science. Recuperado de https://doi.org/10.1021/acscentsci.7b00064 Crumly, C. R. (2013). Morphogenesis. Salem Press Encyclopedia of Science. SalemPress. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=88833290&lang=es&site=eds-live Deasy, B. M., Jankowski, R. J., Payne, T. R., Cao, B., Goff, J. P., Greenberger, J. S., y Huard, J. (2003). Modeling Stem Cell Population Growth: Incorporating Terms for Proliferative Heterogeneity. Stem Cells. Recuperado de https://doi.org/10.1634/stemcells.21-5-536 Determining Cell Vitality | Cayman Chemical. (s.f.). Recuperado de September 17, 2018, Recuperado de https://www.caymanchem.com/news/determining-cell- vitality Esteban Siemens, C. A., Maximilian, L., Staeck, O., Baier, S., Yang Siemens, Y. A., y Tresp, V. (2016). Predicting Clinical Events by Combining Static and Dynamic Information using Recurrent Neural Networks. 2016 IEEE International conference on healthcare informatics (ICHI), 93–101. Recuperado de https://doi.org/10.1109/ICHI.2016.16 Fagan, M. B. (2012). Materia mathematica: Models in stem cell biology. Journal of Experimental and Theoretical Artificial Intelligence, 24(3), 315–327. Recuperado de https://doi.org/10.1080/0952813X.2012.694994 Frangogiannis, N. G., Smith, C. W., y Entman, M. L. (2002). The inflammatory response in myocardial infarction. Cardiovascular Research, 53, 31–47. Recuperado de www.elsevier.com Galvão, V., Miranda, J. G. V., y Ribeiro-dos-Santos, R. (2008). Development of a two-dimensional agent-based model for chronic chagasic cardiomyopathy after stem cell transplantation. Bioinformatics. Recuperado de https://doi.org/10.1093/bioinformatics/btn362 Gastón Fourcade, M. (2008). El Endotelio Vascular. Lecturas Vasculares, 3(4), 508– 513. Recuperado de http://www.sflb.com.ar/revista/2008_03_09-04.pdf Genetic Algorithm Options - MATLAB & Simulink - MathWorks America Latina. (s.f.). Recuperado de https://la.mathworks.com/help/gads/genetic-algorithm- options.html?requestedDomain=true Grabowska, M., Matusik, R., Kimura, M., Igi, A., Hayashizaki, K., Mita, Y., Shinzawa, M., Kadakia, T., Endo, Y., Ogawa, S., Yagi, R., Motohashi, S., Singer, A., Nakayama, T., Pan, Y., Chen, C., Chan, Y., Wang, H., Chien, F., Chen, Y., Liu, J., Yang, M., Z, D. (2018). Cell migration - Latest research and news | Nature. Recuperado de https://www.nature.com/subjects/cell-migration Growth Curves | stemcells.nih.gov. (s.f.). Recuperado de https://stemcells.nih.gov/research/nihresearch/scunit/growthcurves.htm#bg1 Howard, P. (2009). Modeling with ODE, 1–26. Høyem, M. R., Måløy, F., Jakobsen, P., y Brandsdal, B. O. (2015). Stem cell regulation: Implications when differentiated cells regulate symmetric stem cell division. Journal of Theoretical Biology, 380, 203–219. Recuperado de https://doi.org/10.1016/j.jtbi.2015.05.009 Inverno, M., y Saunders, R. (2005). Agent-Based Modelling of Stem Cell Self- organisation in a Niche, 52–68. Kadle, R. L., Abdou, S. A., Villarreal-Ponce, A. P., Soares, M. A., Sultan, D. L., y David, J. A. (2018). Microenvironmental cues enhance mesenchymal stem cell- mediated immunomodulation and regulatory T-cell expansion. Recuperado de https://doi.org/10.1371/journal Kanber, B. (2012). Machine Learning: Introduction to Genetic Algorithms. Recuperado de https://burakkanber.com/blog/machine-learning-genetic- algorithms-part-1-javascript/ Lamrini, B., Della Valle, G., Trelea, I. C., Perrot, N., y Trystram, G. (2012). A new method for dynamic modelling of bread dough kneading based on artificial neural network. Food Control, 26(2), 512–524. Recuperado de https://doi.org/10.1016/j.foodcont.2012.01.011 López, J. A. (2016). Algoritmos Genéticos. Cali, Colombia: Universidad Autónoma de Occidente. Luo, H., Chairperson, C., Aluthge, A., y Drost, J. (2007). Population Modeling by Differential Equations Population Modeling by Differential Equations, (May). Ma, P. X. J., y Elisseeff, J. (2006). Scaffolding in tissue engineering. Boca Raton, Florida Taylor & Francis 2006. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=cat00951a&AN=occ.000024964&lang=es&site=eds-live Macal, C., y North, M. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4, 151–162. Recuperado de https://doi.org/10.1057/jos.2010.3 Marr, B. (2016). The Top 10 AI And Machine Learning Use Cases Everyone Should Know About. Recuperado de https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10- use-cases-for-machine-learning-and-ai/#79bba5a594c9 Martire, A., Bedada, F. B., Uchida, S., Pöling, J., Krüger, M., Warnecke, H., ... Braun, T. (2016). Mesenchymal stem cells attenuate inflammatory processes in the heart and lung via inhibition of TNF signaling. Basic Research in Cardiology, 111(54). Recuperado de https://doi.org/10.1007/s00395-016-0573-2 Matsuoka, F., Takeuchi, I., Agata, H., Kagami, H., Shiono, H., Kiyota, Y., ... Kato, R. (2013). Morphology-Based Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells. PLoS ONE, 8(2). Recuperado de https://doi.org/10.1371/journal.pone.0055082 Murphy, S. V., y Atala, A. (2014). 3D bioprinting of tissues and organs. Nature Biotechnology, 32(8), https://doi.org/10.1038/nbt.2958 773–3785. Recuperado de Neuta, P. A. (2017). comunicación verbal. Cali, Colombia. Oxford Dictionaries Español. (2018). biomaterial | Definición de biomaterial en español de Oxford Dictionaries. Recuperado de https://es.oxforddictionaries.com/definicion/biomaterial Palomero, G., Vásquez, M., Vega, J., Naves, F., y Rodríguez, C. (2000). Lecciones de embriologı́a. (U. de O. Servicio de Publicaciones, Ed.). Servicio de Publicaciones, Universidad de Oviedo. Pojda, Z., Machaj, E., Kurzyk, A., Mazur, S., Debski, T., Gilewicz, J., y Wysocki, J. (2013). [Mesenchymal stem cells]. Postepy Biochemii, 59(2), 187–197. Recuperado de http://www.ncbi.nlm.nih.gov/pubmed/24044283 Porras, A., y Marzo, I. (2010). Apoptosis: una forma controlada de muerte celular. SEBBM Divulgación La Ciencia Al Alcance de La Mano. Roeder, I., y Loeffler, M. (2002). A novel dynamic model of hematopoietic stem cell organization based on the concept of within-tissue plasticity. Experimental Hematology. Recuperado de https://doi.org/10.1016/S0301-472X(02)00832-9 Roeder, I., Loeffler, M., y Glauche, I. (2011). Towards a quantitative understanding of stem cell-niche interaction: Experiments, models, and technologies. Blood Cells, Molecules, and Diseases. Recuperado de https://doi.org/10.1016/j.bcmd.2011.03.001 Roy, S. S., Hsu, C. H., Wen, Z. H., Lin, C. S., y Chakraborty, C. (2010). Understanding hematopoietic stem cell mobility pattern through mathematics. Rivista Di Biologia - Biology Forum. Sancho, F. (2017). Introducción a la Lógica Difusa. Recuperado de August 3, 2017, http://www.cs.us.es/~fsancho/?e=97 SAS OnDemand for Academics | SAS. (s.f.). Recuperado de October 4, 2018, Recuperado de https://www.sas.com/en_us/software/on-demand-for- academics.html Sheposh, R. (2016). Fuzzy logic. Salem Press Encyclopedia of Science. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=87321430&lang=es&site=eds-live Stiehl, T., y Marciniak-Czochra, A. (2011). Characterization of stem cells using mathematical models of multistage cell lineages. Mathematical and Computer Modelling. Recuperado de https://doi.org/10.1016/j.mcm.2010.03.057 Stoddart, M. J. (2011). Cell Viability Assays: Introduction. In Methods in molecular biology (Clifton, N.J.) 740, pp. 1–6. Recuperado de https://doi.org/10.1007/978- 1-61779-108-6_1 Sundnes, J., Lines, G. T., y Tveito, A. (s.f.). E cient solution of ordinary di erential equations modeling electrical activity in cardiac cells. Świątkiewicz, I., Koziński, M., Magielski, P., Fabiszak, T., Kubica, A., Sukiennik, A., ... Kubica, J. (2014). Course of inflammatory activation during acute myocardial infarction in patients with preserved left ventricular systolic function. Folia Medica Copernicana, 2(1), 6–18. Recuperado de www.fmc.viamedica.pl Tabatabai, M. A., Bursac, Z., Eby, W. M., y Singh, K. P. (2011). Mathematical modeling of stem cell proliferation. Medical & Biological Engineering & Computing. Recuperado de https://doi.org/10.1007/s11517-010-0686-y Tabatabai, M., Williams, D. K., y Bursac, Z. (2005). Hyperbolastic growth models: Theory and application. Theoretical Biology and Medical Modelling. Recuperado de https://doi.org/10.1186/1742-4682-2-14 Tanaka, N., Yamashita, T., Sato, A., Vogel, V., y Tanaka, Y. (2017). Simple agarose micro-confinement array and machine-learning-based classification for analyzing the patterned differentiation of mesenchymal stem cells. PLoS ONE, 12(4), 1–17. Recuperado de https://doi.org/10.1371/journal.pone.0173647 Tantawi PhD, R. (2013). Machine learning. Salem Press Encyclopedia. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=90558380&lang=es&site=eds-live Thrivikraman, G., Boda, S. K., y Basu, B. (2018). Unraveling the mechanistic effects of electric field stimulation towards directing stem cell fate and function: A tissue engineering perspective. Biomaterials, 150, 60–86. Recuperado de https://doi.org/10.1016/j.biomaterials.2017.10.003 Topalovic, M., Exadaktylos, V., Decramer, M., Troosters, T., Berckmans, D., y Janssens, W. (2014). Modelling the dynamics of expiratory airflow to describe chronic obstructive pulmonary disease. Medical & Biological Engineering & Computing, 52(12), 997–1006. Recuperado de https://doi.org/10.1007/s11517- 014-1202-6 Ungvarsky, J. (2017). Regenerative medicine. Salem Press Encyclopedia of Health. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=87323211&lang=es&site=eds-live Urbas, J. V. (2013). Neural Networks. Salem Press Encyclopedia of Science. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=89317103&lang=es&site=eds-live Wang, Y., Wang, F., Zhao, H., Zhang, X., Chen, H., y Zhang, K. (2014). Human adipose-derived mesenchymal stem cells are resistant to HBV infection during differentiation into hepatocytes in vitro. International Journal of Molecular Sciences, 15(4), 6096–6110. Recuperado de https://doi.org/10.3390/ijms15046096 Wilensky, U. (1999). The NetLogo 6 . 0 . 2 User Manual Table of Contents Table of Contents What is NetLogo ? Northwestern University. Zhao, Y., Bucur, O., Irshad, H., Chen, F., Weins, A., Stancu, A. L., ... Boyden, E. S. (2017). Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy. Nature Biotechnology, 35(8), 757–764. Recuperado de https://doi.org/10.1038/nbt.3892
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spelling Rojas Arciniegas, Álvaro Josévirtual::4467-1Ramírez López, Diana Victoria61eab05fa05105f98531e3e664b0f26a-1Ingeniero MecatrónicoUniversidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2018-12-04T14:14:39Z2018-12-04T14:14:39Z2018-09-12http://hdl.handle.net/10614/10497The following document presents the development of an agent based model which simulates the behavior of mesenchymal stem cells (MSC), cardiomyocites and endothelial cells in a 3D-printed biodevice which is implanted on the infarcted myocardium as part of a potential treatment for the regeneration of the affected tissue. Examples of computational modeling applied to the behavior of cells are shown and some 3D bioprinting techniques are explained, such as microextrusion, which is used for the fabrication of the biodevices. The context for which the model has been thought is also described, taking into account the use of 3D printing as fabrication technique and the in vitro tests that would validate the results. After this, some machine learning techniques are presented, given that they were considered as alternatives to develop the model. Following the implementation of an ordinary differential equation-based model, the use of agent based modeling was considered as a tool that would better allow including the cellular microenvironment characteristics in the model. Thus, the development of the model with the software Netlogo, its functioning and the result’s visualization are explained step by step. At last, some results are shown, which were obtained after running determined experiments defined through an experimental design and their analysis, which shows that the model can simulate processes that occur in the cellular microenvironment of the infarcted myocardium through the interactions of cells and that it allows the observation of emergent behaviors that can be helpful to determine the characteristics that favor the success that is expected the treatment with the 3D- printed biodevice.tA continuación se presenta el desarrollo de un modelo basado en agentes en el cual se simula el comportamiento de células madre mesenquimales (MSC), cardiomiocitos y células endoteliales en un biodispositivo impreso en 3D que se implantará en el miocardio infartado como parte de un potencial tratamiento el cual busca la regeneración del tejido afectado. Se muestran ejemplos de modelado computacional aplicado al comportamiento de células y se explica el funcionamiento de algunas técnicas de bioimpresión 3D, como la microextrusión utilizada en la fabricación del biodispositivo. De igual manera, se describe el contexto para el cual se ha decidido realizar el modelo, teniendo en cuenta el uso de la impresión 3D como técnica de fabricación y las pruebas in vitro que validarían sus resultados. Seguido a esto se presentan algunas técnicas de machine learning que fueron consideradas como alternativas para realizar el modelo. Después de la implementación de un modelo basado en ecuaciones diferenciales se decide optar por el modelado basado en agentes, considerando que es una herramienta que permitirá incluir en el modelo las características del microambiente celular. Por consiguiente, se describe paso a paso la realización del modelo en el software Netlogo, su funcionamiento y la visualización de los resultados. Finalmente, se muestran algunos resultados obtenidos tras la realización de experimentos definidos mediante un diseño experimental y su análisis, el cual muestra que el modelo puede simular procesos del microambiente celular del miocardio infartado a través de las interacciones de las células y que permite observar comportamientos emergentes que pueden ayudar a determinar las características que favorecen la obtención del éxito esperado mediante el tratamiento con el biodispositivo impreso en 3DProyecto de grado (Ingeniero Mecatrónico)— Universidad Autónoma de Occidente, 2018.PregradoIngeniero(a) Mecatrónico(a)application/pdf98 páginasspaUniversidad Autónoma de OccidenteIngeniería MecatrónicaDepartamento de Automática y ElectrónicaFacultad de IngenieríaDerechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2instname:Universidad Autónoma de Occidentereponame:Repositorio Institucional UAO3D Printering: XT-CF20 Carbon Fiber Filament Review | Hackaday. (s.f.). Recuperado de https://hackaday.com/2016/09/07/3d-printering-xt-cf20-carbon- fiber-filament-review/ Álvarez, A. (2013). Modelización. computación y matemáticas contra el cáncer. Bastidas, O. (s.f.). Technical Note -Neubauer Chamber Cell Counting. Recuperado de www.celeromics.com Biba, M., Xhafa, F., Esposito, F., y Ferilli, S. (2011). Using Machine Learning Techniques for Modelling and Simulation of Metabolic Networks. International Conference on Complex, Intelligent, and Software Intensive Systems, 85–92. https://doi.org/10.1109/CISIS.2011.22 Briers, D., Haghighi, I., White, D., Kemp, M. L., y Belta, C. (2016). Pattern synthesis in a 3D agent-based model of stem cell differentiation. In 2016 IEEE 55th Conference on Decision and Control (CDC). Recuperado de https://doi.org/10.1109/CDC.2016.7798907 Cano, G., García-Rodríguez, J., Orts, S., García-García, A., Peña-García, J., Pérez- Garrido, A., y Pérez-Sánchez, H. (2017). Predicción de solubilidad de fármacos usando máquinas de soporte vectorial sobre unidades de procesamiento gráfico. Revista Internacional de Metodos Numericos Para Calculo Y Diseno En Ingenieria. Recuperado de https://doi.org/10.1016/j.rimni.2015.12.001 Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., y Jensen, K. F. (2017). Prediction of Organic Reaction Outcomes Using Machine Learning. ACS Central Science. Recuperado de https://doi.org/10.1021/acscentsci.7b00064 Crumly, C. R. (2013). Morphogenesis. Salem Press Encyclopedia of Science. SalemPress. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=88833290&lang=es&site=eds-live Deasy, B. M., Jankowski, R. J., Payne, T. R., Cao, B., Goff, J. P., Greenberger, J. S., y Huard, J. (2003). Modeling Stem Cell Population Growth: Incorporating Terms for Proliferative Heterogeneity. Stem Cells. Recuperado de https://doi.org/10.1634/stemcells.21-5-536 Determining Cell Vitality | Cayman Chemical. (s.f.). Recuperado de September 17, 2018, Recuperado de https://www.caymanchem.com/news/determining-cell- vitality Esteban Siemens, C. A., Maximilian, L., Staeck, O., Baier, S., Yang Siemens, Y. A., y Tresp, V. (2016). Predicting Clinical Events by Combining Static and Dynamic Information using Recurrent Neural Networks. 2016 IEEE International conference on healthcare informatics (ICHI), 93–101. Recuperado de https://doi.org/10.1109/ICHI.2016.16 Fagan, M. B. (2012). Materia mathematica: Models in stem cell biology. Journal of Experimental and Theoretical Artificial Intelligence, 24(3), 315–327. Recuperado de https://doi.org/10.1080/0952813X.2012.694994 Frangogiannis, N. G., Smith, C. W., y Entman, M. L. (2002). The inflammatory response in myocardial infarction. Cardiovascular Research, 53, 31–47. Recuperado de www.elsevier.com Galvão, V., Miranda, J. G. V., y Ribeiro-dos-Santos, R. (2008). Development of a two-dimensional agent-based model for chronic chagasic cardiomyopathy after stem cell transplantation. Bioinformatics. Recuperado de https://doi.org/10.1093/bioinformatics/btn362 Gastón Fourcade, M. (2008). El Endotelio Vascular. Lecturas Vasculares, 3(4), 508– 513. Recuperado de http://www.sflb.com.ar/revista/2008_03_09-04.pdf Genetic Algorithm Options - MATLAB & Simulink - MathWorks America Latina. (s.f.). Recuperado de https://la.mathworks.com/help/gads/genetic-algorithm- options.html?requestedDomain=true Grabowska, M., Matusik, R., Kimura, M., Igi, A., Hayashizaki, K., Mita, Y., Shinzawa, M., Kadakia, T., Endo, Y., Ogawa, S., Yagi, R., Motohashi, S., Singer, A., Nakayama, T., Pan, Y., Chen, C., Chan, Y., Wang, H., Chien, F., Chen, Y., Liu, J., Yang, M., Z, D. (2018). Cell migration - Latest research and news | Nature. Recuperado de https://www.nature.com/subjects/cell-migration Growth Curves | stemcells.nih.gov. (s.f.). Recuperado de https://stemcells.nih.gov/research/nihresearch/scunit/growthcurves.htm#bg1 Howard, P. (2009). Modeling with ODE, 1–26. Høyem, M. R., Måløy, F., Jakobsen, P., y Brandsdal, B. O. (2015). Stem cell regulation: Implications when differentiated cells regulate symmetric stem cell division. Journal of Theoretical Biology, 380, 203–219. Recuperado de https://doi.org/10.1016/j.jtbi.2015.05.009 Inverno, M., y Saunders, R. (2005). Agent-Based Modelling of Stem Cell Self- organisation in a Niche, 52–68. Kadle, R. L., Abdou, S. A., Villarreal-Ponce, A. P., Soares, M. A., Sultan, D. L., y David, J. A. (2018). Microenvironmental cues enhance mesenchymal stem cell- mediated immunomodulation and regulatory T-cell expansion. Recuperado de https://doi.org/10.1371/journal Kanber, B. (2012). Machine Learning: Introduction to Genetic Algorithms. Recuperado de https://burakkanber.com/blog/machine-learning-genetic- algorithms-part-1-javascript/ Lamrini, B., Della Valle, G., Trelea, I. C., Perrot, N., y Trystram, G. (2012). A new method for dynamic modelling of bread dough kneading based on artificial neural network. Food Control, 26(2), 512–524. Recuperado de https://doi.org/10.1016/j.foodcont.2012.01.011 López, J. A. (2016). Algoritmos Genéticos. Cali, Colombia: Universidad Autónoma de Occidente. Luo, H., Chairperson, C., Aluthge, A., y Drost, J. (2007). Population Modeling by Differential Equations Population Modeling by Differential Equations, (May). Ma, P. X. J., y Elisseeff, J. (2006). Scaffolding in tissue engineering. Boca Raton, Florida Taylor & Francis 2006. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=cat00951a&AN=occ.000024964&lang=es&site=eds-live Macal, C., y North, M. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4, 151–162. Recuperado de https://doi.org/10.1057/jos.2010.3 Marr, B. (2016). The Top 10 AI And Machine Learning Use Cases Everyone Should Know About. Recuperado de https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10- use-cases-for-machine-learning-and-ai/#79bba5a594c9 Martire, A., Bedada, F. B., Uchida, S., Pöling, J., Krüger, M., Warnecke, H., ... Braun, T. (2016). Mesenchymal stem cells attenuate inflammatory processes in the heart and lung via inhibition of TNF signaling. Basic Research in Cardiology, 111(54). Recuperado de https://doi.org/10.1007/s00395-016-0573-2 Matsuoka, F., Takeuchi, I., Agata, H., Kagami, H., Shiono, H., Kiyota, Y., ... Kato, R. (2013). Morphology-Based Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells. PLoS ONE, 8(2). Recuperado de https://doi.org/10.1371/journal.pone.0055082 Murphy, S. V., y Atala, A. (2014). 3D bioprinting of tissues and organs. Nature Biotechnology, 32(8), https://doi.org/10.1038/nbt.2958 773–3785. Recuperado de Neuta, P. A. (2017). comunicación verbal. Cali, Colombia. Oxford Dictionaries Español. (2018). biomaterial | Definición de biomaterial en español de Oxford Dictionaries. Recuperado de https://es.oxforddictionaries.com/definicion/biomaterial Palomero, G., Vásquez, M., Vega, J., Naves, F., y Rodríguez, C. (2000). Lecciones de embriologı́a. (U. de O. Servicio de Publicaciones, Ed.). Servicio de Publicaciones, Universidad de Oviedo. Pojda, Z., Machaj, E., Kurzyk, A., Mazur, S., Debski, T., Gilewicz, J., y Wysocki, J. (2013). [Mesenchymal stem cells]. Postepy Biochemii, 59(2), 187–197. Recuperado de http://www.ncbi.nlm.nih.gov/pubmed/24044283 Porras, A., y Marzo, I. (2010). Apoptosis: una forma controlada de muerte celular. SEBBM Divulgación La Ciencia Al Alcance de La Mano. Roeder, I., y Loeffler, M. (2002). A novel dynamic model of hematopoietic stem cell organization based on the concept of within-tissue plasticity. Experimental Hematology. Recuperado de https://doi.org/10.1016/S0301-472X(02)00832-9 Roeder, I., Loeffler, M., y Glauche, I. (2011). Towards a quantitative understanding of stem cell-niche interaction: Experiments, models, and technologies. Blood Cells, Molecules, and Diseases. Recuperado de https://doi.org/10.1016/j.bcmd.2011.03.001 Roy, S. S., Hsu, C. H., Wen, Z. H., Lin, C. S., y Chakraborty, C. (2010). Understanding hematopoietic stem cell mobility pattern through mathematics. Rivista Di Biologia - Biology Forum. Sancho, F. (2017). Introducción a la Lógica Difusa. Recuperado de August 3, 2017, http://www.cs.us.es/~fsancho/?e=97 SAS OnDemand for Academics | SAS. (s.f.). Recuperado de October 4, 2018, Recuperado de https://www.sas.com/en_us/software/on-demand-for- academics.html Sheposh, R. (2016). Fuzzy logic. Salem Press Encyclopedia of Science. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=87321430&lang=es&site=eds-live Stiehl, T., y Marciniak-Czochra, A. (2011). Characterization of stem cells using mathematical models of multistage cell lineages. Mathematical and Computer Modelling. Recuperado de https://doi.org/10.1016/j.mcm.2010.03.057 Stoddart, M. J. (2011). Cell Viability Assays: Introduction. In Methods in molecular biology (Clifton, N.J.) 740, pp. 1–6. Recuperado de https://doi.org/10.1007/978- 1-61779-108-6_1 Sundnes, J., Lines, G. T., y Tveito, A. (s.f.). E cient solution of ordinary di erential equations modeling electrical activity in cardiac cells. Świątkiewicz, I., Koziński, M., Magielski, P., Fabiszak, T., Kubica, A., Sukiennik, A., ... Kubica, J. (2014). Course of inflammatory activation during acute myocardial infarction in patients with preserved left ventricular systolic function. Folia Medica Copernicana, 2(1), 6–18. Recuperado de www.fmc.viamedica.pl Tabatabai, M. A., Bursac, Z., Eby, W. M., y Singh, K. P. (2011). Mathematical modeling of stem cell proliferation. Medical & Biological Engineering & Computing. Recuperado de https://doi.org/10.1007/s11517-010-0686-y Tabatabai, M., Williams, D. K., y Bursac, Z. (2005). Hyperbolastic growth models: Theory and application. Theoretical Biology and Medical Modelling. Recuperado de https://doi.org/10.1186/1742-4682-2-14 Tanaka, N., Yamashita, T., Sato, A., Vogel, V., y Tanaka, Y. (2017). Simple agarose micro-confinement array and machine-learning-based classification for analyzing the patterned differentiation of mesenchymal stem cells. PLoS ONE, 12(4), 1–17. Recuperado de https://doi.org/10.1371/journal.pone.0173647 Tantawi PhD, R. (2013). Machine learning. Salem Press Encyclopedia. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=90558380&lang=es&site=eds-live Thrivikraman, G., Boda, S. K., y Basu, B. (2018). Unraveling the mechanistic effects of electric field stimulation towards directing stem cell fate and function: A tissue engineering perspective. Biomaterials, 150, 60–86. Recuperado de https://doi.org/10.1016/j.biomaterials.2017.10.003 Topalovic, M., Exadaktylos, V., Decramer, M., Troosters, T., Berckmans, D., y Janssens, W. (2014). Modelling the dynamics of expiratory airflow to describe chronic obstructive pulmonary disease. Medical & Biological Engineering & Computing, 52(12), 997–1006. Recuperado de https://doi.org/10.1007/s11517- 014-1202-6 Ungvarsky, J. (2017). Regenerative medicine. Salem Press Encyclopedia of Health. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=87323211&lang=es&site=eds-live Urbas, J. V. (2013). Neural Networks. Salem Press Encyclopedia of Science. Salem Press. Recuperado de http://ezproxy.uao.edu.co:2048/login?url=http://search.ebscohost.com/login.as px?direct=true&db=ers&AN=89317103&lang=es&site=eds-live Wang, Y., Wang, F., Zhao, H., Zhang, X., Chen, H., y Zhang, K. (2014). Human adipose-derived mesenchymal stem cells are resistant to HBV infection during differentiation into hepatocytes in vitro. International Journal of Molecular Sciences, 15(4), 6096–6110. Recuperado de https://doi.org/10.3390/ijms15046096 Wilensky, U. (1999). The NetLogo 6 . 0 . 2 User Manual Table of Contents Table of Contents What is NetLogo ? Northwestern University. Zhao, Y., Bucur, O., Irshad, H., Chen, F., Weins, A., Stancu, A. L., ... Boyden, E. S. (2017). Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy. Nature Biotechnology, 35(8), 757–764. Recuperado de https://doi.org/10.1038/nbt.3892Ingeniería MecatrónicaInteligencia artificialBioingenieríaInfarto cardíacoImpresión en tercera dimensiónCélulas madre mesenquimalesBiodispositivoModelado computacional del comportamiento de las células madre en un biodispositivo impreso en 3DTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttps://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Publicationhttps://scholar.google.com/citations?user=Jk__bOIAAAAJ&hl=envirtual::4467-10000-0001-9242-799Xvirtual::4467-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000657956virtual::4467-15d4f6e65-758a-44ee-be02-f12af232a478virtual::4467-15d4f6e65-758a-44ee-be02-f12af232a478virtual::4467-1TEXTT08156.pdf.txtT08156.pdf.txtExtracted texttext/plain137916https://red.uao.edu.co/bitstreams/4a87c4a5-f464-4f1c-8767-b0e73a314c0b/downloade5cc3d17968ce770406a2e0b8bd85081MD57TA8156.pdf.txtTA8156.pdf.txtExtracted texttext/plain4159https://red.uao.edu.co/bitstreams/712e138b-5dcd-4d7b-ae97-936612527259/download0275431bea02a36310fa33fb53439d79MD59THUMBNAILT08156.pdf.jpgT08156.pdf.jpgGenerated Thumbnailimage/jpeg6994https://red.uao.edu.co/bitstreams/f817398b-3412-4719-91ab-fdf1fa2e324e/download8cc08d3dc3ed26dd3f0a2ced6cfd7848MD58TA8156.pdf.jpgTA8156.pdf.jpgGenerated Thumbnailimage/jpeg12969https://red.uao.edu.co/bitstreams/ed55f0f9-d99b-4149-97ed-28a38fbfceb1/downloadb2bca8ff06814d30ff678cfb68ff320bMD510CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908https://red.uao.edu.co/bitstreams/d5819d3f-8363-4af9-8284-3e99c54a47a2/download0175ea4a2d4caec4bbcc37e300941108MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/c43147b6-7cac-4aff-bb82-91a444d9ef51/download20b5ba22b1117f71589c7318baa2c560MD54ORIGINALT08156.pdfT08156.pdfapplication/pdf3097908https://red.uao.edu.co/bitstreams/81977e84-92ca-4174-baf7-0e1d522f4865/download137c9ec1d06471a464034477fab6938bMD55TA8156.pdfTA8156.pdfapplication/pdf402555https://red.uao.edu.co/bitstreams/ce578621-31b4-4d81-bf04-9b0fea86cfa7/downloaddce7acb2314205a5bf101eeacb9160dfMD5610614/10497oai:red.uao.edu.co:10614/104972024-03-14 10:52:17.574https://creativecommons.org/licenses/by/4.0/Derechos Reservados - Universidad Autónoma de Occidenteopen.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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