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dc.creatorMolina Mora, José Arturo
dc.creatorQuirós Barrantes, Steve
dc.creatorKop Montero, Mariana
dc.creatorMora Rodríguez, Rodrigo Antonio
dc.creatorCrespo Mariño, Juan Luis
dc.date.accessioned2022-03-02T13:59:53Z
dc.date.available2022-03-02T13:59:53Z
dc.date.issued2017
dc.identifier.citationhttps://ieeexplore.ieee.org/document/7985532es_ES
dc.identifier.urihttps://hdl.handle.net/10669/85939
dc.descriptionForma parte de los trabajos presentado en el International Conference and Workshop on Bioinspired Intelligence (IWOBI). IEEE, Estados Unidos. Julio de 2017.es_ES
dc.description.abstractSphingolipid (SL) signaling pathway is a complex biological system able to integrate different types of cellular stress signals related to induction of cell death pathways with special interest in cancer. This makes of the SL pathway a promising sensor of chemosensitivity and a target hub to overcome resistance. However, it is unclear how chemotherapeutic drugs can disturb the SL pathway and how the SL content modulates cellular fate. A hybrid mathematical model was proposed in order to integrate i) the metabolism of SL analogue (SM-BODIPY) modeled by an ordinary differential equation (ODE) approach, ii) a Gaussian mixture model (GMM) of the fluorescence features to identify how the SL pathway senses the effect of chemotherapeutic drugs and iii) a fuzzy logic model (FLM) to associate SL composition with cell viability by semi-quantitative rules. Altogether, this hybrid model approach was able to predict the cell viability of double experimental perturbations with chemotherapy, indicating that the SL pathway is a promising sensor to design strategies to overcome drug resistance in cancer.es_ES
dc.language.isoenges_ES
dc.source2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI). IEEE Xplore, pp.80-85.es_ES
dc.subjectFuzzy logices_ES
dc.subjectCanceres_ES
dc.subjectGMMes_ES
dc.subjectEDOes_ES
dc.subjectSphingolipidses_ES
dc.titleHybrid mathematical modeling decodes the complexity of sphingolipid pathway to predict chemosensitivityes_ES
dc.typecomunicación de congresoes_ES
dc.identifier.doi10.1109/IWOBI.2017.7985532
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)es_ES
dc.description.procedenceUCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologíaes_ES


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