Hybrid mathematical modeling decodes the complexity of sphingolipid pathway to predict chemosensitivity
dc.creator | Molina Mora, José Arturo | |
dc.creator | Quirós Barrantes, Steve | |
dc.creator | Kop Montero, Mariana | |
dc.creator | Mora Rodríguez, Rodrigo Antonio | |
dc.creator | Crespo Mariño, Juan Luis | |
dc.date.accessioned | 2022-03-02T13:59:53Z | |
dc.date.available | 2022-03-02T13:59:53Z | |
dc.date.issued | 2017 | |
dc.description | Forma 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.abstract | Sphingolipid (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.description.procedence | UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET) | es_ES |
dc.description.procedence | UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiología | es_ES |
dc.identifier.citation | https://ieeexplore.ieee.org/document/7985532 | es_ES |
dc.identifier.doi | 10.1109/IWOBI.2017.7985532 | |
dc.identifier.uri | https://hdl.handle.net/10669/85939 | |
dc.language.iso | eng | es_ES |
dc.rights | acceso embargado | |
dc.source | 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI). IEEE Xplore, pp.80-85. | es_ES |
dc.subject | Fuzzy logic | es_ES |
dc.subject | Cancer | es_ES |
dc.subject | GMM | es_ES |
dc.subject | EDO | es_ES |
dc.subject | Sphingolipids | es_ES |
dc.title | Hybrid mathematical modeling decodes the complexity of sphingolipid pathway to predict chemosensitivity | es_ES |
dc.type | comunicación de congreso | es_ES |