Modelo exacto difuso de un cultivo por lote alimentado

Mexican Consejo Nacional de Ciencia y Tecnología (CONACYT) - projectos 46538, 41148Bolsa de doutoramento 7066

Exact fuzzy observer for a baker’s yeast fermentation process

The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A continuous baker’s yeast culture is divided in two sub-models: a respiro fermentative with ethanol production and a respirative with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI’s). The observer premise variables depend on the state variables estimated by the fuzzy observer.Mexican Consejo Nacional de Ciencia y Tecnología (CONACYT) - programa 46358, 41148, Ph.D. scholarship 7066

Takagi-Sugeno multiple-model controller for a continuous baking yeast fermentation process

The purpose of this work is to design a fuzzy integral controller to force the switching of a bioprocess between two different metabolic states. A continuous baking yeast culture is divided in two sub-models: a respiro fermentative with ethanol production and a respirative with ethanol consumption. The switching between both different metabolic states is achieved by means of tracking a reference substrate signal. A substrate fuzzy integral controller model using sector nonlinearity was built for both nonlinear models.Mexican Consejo Nacional de Ciencia y Tecnología (CONACyT) - programa 46538, 41148Ph.D. Schoolarship 7066

Takagi-Sugeno fuzzy observer for a switching bioprocess : sector nonlinearity approach

Mexican Consejo Nacional de Ciencia y Tecnología (CONACyT)

Exact fuzzy observer for a baker’s yeast fed-batch fermentation process

The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.Mexican Consejo Nacional de Ciencia y Tecnología (CONACyT) - under grants 46538, 41148 and the Ph.D. Schoolarship 70662

Modelo exacto difuso de un proceso fermentativo conmutado

Los modelos matemáticos de los procesos fermentativos son complejos y difíciles de trabajar, debido a las no linealidades presentes en el modelo. En este trabajo se realizó un modelo difuso Takagi-Sugeno, de un cultivo en continuo de la levadura de panificación Saccharomyces cerevisiae. El modelo difuso se basó en la técnica de sectores no lineales, él cual permite representar exactamente a un sistema no lineal mediante subsistemas lineales. Una característica importante del modelo fermentativo, es que puede ser dividido en dos modelos parciales: uno respiro-fermentativo (RF) con producción de etanol y otro respirativo (R) con consumo de etanol. La condición para la transición entre los modelos parciales depende de la producción o consumo del etanol. Es necesario evaluar la elección de las variables premisas, ya que estas repercuten directamente en la observabilidad y controlabilidad del sistema. Del modelo exacto difuso obtenido, es posible construir observadores y controladores difusos.Consejo Nacional de Ciencia y Tecnología (CONACYT)

Enhancing dendritic cell immunotherapy for melanoma using a simple mathematical model

ABSTRACT Background: The immunotherapy using dendritic cells (DCs) against different varieties of cancer is an approach that has been previously explored which induces a specific immune response. This work presents a mathematical model of DCs immunotherapy for melanoma in mice based on work by Experimental Immunotherapy Laboratory of the Medicine Faculty in the Universidad Autonoma de Mexico (UNAM). Method: The model is a five delay differential equation (DDEs) which represents a simplified view of the immunotherapy mechanisms. The mathematical model takes into account the interactions between tumor cells, dendritic cells, naive cytotoxic T lymphocytes cells (inactivated cytotoxic cells), effector cells (cytotoxic T activated cytotoxic cells) and transforming growth factor β cytokine (TGF − β). The model is validated comparing the computer simulation results with biological trial results of the immunotherapy developed by the research group of UNAM. Results: The results of the growth of tumor cells obtained by the control immunotherapy simulation show a similar amount of tumor cell population than the biological data of the control immunotherapy. Moreover, comparing the increase of tumor cells obtained from the immunotherapy simulation and the biological data of the immunotherapy applied by the UNAM researchers obtained errors of approximately 10 %. This allowed us to use the model as a framework to test hypothetical treatments. The numerical simulations suggest that by using more doses of DCs and changing the infusion time, the tumor growth decays compared with the current immunotherapy. In addition, a local sensitivity analysis is performed; the results show that the delay in time “τ ”, the maximal growth rate of tumor “r” and the maximal efficiency of tumor cytotoxic cells rate “aT” are the most sensitive model parameters. Conclusion: By using this mathematical model it is possible to simulate the growth of the tumor cells with or without immunotherapy using the infusion protocol of the UNAM researchers, to obtain a good approximation of the biological trials data. It is worth mentioning that by manipulating the different parameters of the model the effectiveness of the immunotherapy may increase. This last suggests that different protocols could be implemented by the Immunotherapy Laboratory of UNAM in order to improve their results