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28 results on '"Vega, Carlos J."'

1. Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks

Author

Suarez, Oscar J., Macias-Garcia, Edgar, Vega, Carlos J., Peñaloza, Yersica C., Díaz, Nicolás Hernández, Garrido, Victor M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Orjuela-Cañón, Alvaro David, editor, Lopez, Jesus, editor, Arias-Londoño, Julian David, editor, and Figueroa-García, Juan Carlos, editor

Published
2023
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2. Pinning Control to Regulate Cellular Response in Cancer for the p53-Mdm2 Genetic Regulatory Network

Author

Suarez, Oscar J., Vega, Carlos J., Sanchez, Edgar N., Chen, Guanrong, González-Santiago, Ana E., Rodríguez-Jorge, Otoniel, Alanis, Alma Y., Hernandez-Vargas, Esteban A., Zelinka, Ivan, Series Editor, Adamatzky, Andrew, Series Editor, Chen, Guanrong, Series Editor, Abraham, Ajith, Editorial Board Member, Lucia, Ana, Editorial Board Member, Burguillo, Juan C., Editorial Board Member, Čelikovský, Sergej, Editorial Board Member, Chadli, Mohammed, Editorial Board Member, Corchado, Emilio, Editorial Board Member, Davendra, Donald, Editorial Board Member, Ilachinski, Andrew, Editorial Board Member, Lampinen, Jouni, Editorial Board Member, Middendorf, Martin, Editorial Board Member, Ott, Edward, Editorial Board Member, Pan, Linqiang, Editorial Board Member, Păun, Gheorghe, Editorial Board Member, Richter, Hendrik, Editorial Board Member, Rodriguez-Aguilar, Juan A., Editorial Board Member, Rössler, Otto, Editorial Board Member, Snasel, Vaclav, Editorial Board Member, Vondrák, Ivo, Editorial Board Member, Zenil, Hector, Editorial Board Member, and Balaz, Igor, editor

Published
2022
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15. Pinning control for the p53-Mdm2 network dynamics regulated by p14ARF

Author

Suarez, Oscar J., Vega, Carlos J., Sanchez, Edgar N., González-Santiago, Ana E., Rodríguez-Jorge, Otoniel, Alanis, Alma Y., Chen, Guanrong, Hernandez-Vargas, Esteban Abelardo, Suarez, Oscar J., Vega, Carlos J., Sanchez, Edgar N., González-Santiago, Ana E., Rodríguez-Jorge, Otoniel, Alanis, Alma Y., Chen, Guanrong, and Hernandez-Vargas, Esteban Abelardo

Abstract

p53 regulates the cellular response to genotoxic damage and prevents carcinogenic events. Theoretical and experimental studies state that the p53-Mdm2 network constitutes the core module of regulatory interactions activated by cellular stress induced by a variety of signaling pathways. In this paper, a strategy to control the p53-Mdm2 network regulated by p14ARF is developed, based on the pinning control technique, which consists into applying local feedback controllers to a small number of nodes (pinned ones) in the network. Pinned nodes are selected on the basis of their importance level in a topological hierarchy, their degree of connectivity within the network, and the biological role they perform. In this paper, two cases are considered. For the first case, the oscillatory pattern under gamma-radiation is recovered; afterward, as the second case, increased expression of p53 level is taken into account. For both cases, the control law is applied to p14ARF (pinned node based on a virtual leader methodology), and overexpressed Mdm2-mediated p53 degradation condition is considered as carcinogenic initial behavior. The approach in this paper uses a computational algorithm, which opens an alternative path to understand the cellular responses to stress, doing it possible to model and control the gene regulatory network dynamics in two different biological contexts. As the main result of the proposed control technique, the two mentioned desired behaviors are obtained.

Published
2020

28. Trajectory Tracking on Complex Networks Via Inverse Optimal Pinning Control.

Author

Vega, Carlos J., Suarez, Oscar J., Sanchez, Edgar N., Chen, Guanrong, Elvira-Ceja, Santiago, and Rodriguez-Castellanos, David

Subjects
*CHAOS theory, *DIFFERENTIABLE dynamical systems, *SYSTEMS theory, *NONLINEAR theories, *CHAOS synchronization
Abstract

In this technical note, a novel approach to trajectory tracking control of a complex network is presented, based on the idea and technique of inverse optimal pinning control. A control law that stabilizes the tracking error dynamics is developed. This controller allows different coupling strengths, and simultaneously minimizing an associated cost functional. Chaotic systems are considered to illustrate the applicability of the new methodology and the proposed controller effectiveness via simulations. [ABSTRACT FROM AUTHOR]

Published
2019
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