Degradación anormal de p53 e inducción de apoptosis en la red p53-MDM2 usando la estrategia de control tipo PIN

Oscar Javier  Suarez; Carlos J.  Vega; Edgar N.  Sanchez; Ana E.  González Santiago; Otoniel  Rodríguez Jorge; Aldo  Pardo Garcia

doi:10.24054/rcta.v2i32.101

Autores/as

Oscar Javier Suarez Centro de Investigación y de Estudios Avanzados del IPN
Carlos J. Vega Centro de Investigación y de Estudios Avanzados del IPN
Edgar N. Sanchez Centro de Investigación y de Estudios Avanzados del IPN
Ana E. González Santiago Universidad de Guadalajara
Otoniel Rodríguez Jorge Universidad Autónoma del Estado de Morelos
Aldo Pardo Garcia Universidad de Pamplona

DOI:

https://doi.org/10.24054/rcta.v2i32.101

Palabras clave:

Redes de regulación genética, redes complejas, control tipo PIN, p53, Mdm2

Resumen

Este artículo presenta el control tipo “PIN” para regular la actividad de la red p53-Mdm2. Esta red considera la degradación de p53 mediada por el incremento de Mdm2, el cual perturba la respuesta de estrés normal de p53. El modelo considera tres proteínas: p53, Mdm2 y ARF. p53 es regulado a través de un ciclo de retroalimentación que involucra su gen objetivo Mdm2 y un regulador indirecto ARF. Se presentan dos escenarios. Para el primer escenario, la red responde a un incremento de Mdm2 y una baja regulación de p53 sin ninguna entrada externa; luego, en el segundo escenario apoptosis es inducido por el control tipo “PIN”. El comportamiento dinámico de la red y la efectividad del controlador propuesto son ilustrados vía simulaciones.

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Citas

Angelin-Bonnet, O., Biggs, P., and Vignes, M. (2018). Gene regulatory networks: a primer in biological processes and statistical modelling. arXiv:1805.01098.

Barabasi, A. and Albert, R. (1999). Emergence of scaling in random networks. Science, Vol. 286, No. 5439.

Cao, J., Qi, X., and Zhao, H. (2012). Modeling gene regulation networks using ordinary differential equations. In Next Generation Microarray Bioinformatics.

Chen, G., Wang, X., and Li, X. (2014). Fundamentals of complex networks: models, structures and dynamics. John Wiley & Sons.

Cohen, R. and Havlin, S. (2010). Complex networks: structure, robustness and function. Cambridge University Press.

De Jong, H. (2002). Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology, Vol. 9, No. 1.

Dulin, D., Lipfert, J., Moolman, M. C., and Dekker, N. H. (2013). Studying genomic processes at the single-molecule level: introducing the tools and applications. Nature Reviews Genetics, Vol. 14, No. 1.

Efeyan, A., and Serrano, M. (2007). p53: guardian of the genome and policeman of the oncogenes. Cell cycle, Vol. 6, No. 9.

Haupt, Y., Maya, R., Kazaz, A., and Oren, M. (1997). Mdm2 promotes the rapid degradation of p53. Nature, Vol. 387, No. 6630.

Hu, W., Feng, Z., and Levine, A. J. (2012). The regulation of multiple p53 stress responses is mediated through MDM2. Genes & Cancer, Vol. 3, No. 3-4.

Kessis, T., Slebos, R., Nelson, W., Kastan, M., Plunkett, B., Han, S., Lorinez, A., Hedrick, L., and Cho, K. (1993). Human papillomavirus 16 E6 expression disrupts the p53-mediated cellular response to DNA damage. Proceedings of the National Academy of Sciences, Vol. 90, No. 9.

Khalil, H. (1996). Nonlinear systems. Prentice-Hall, Vol. 2, No. 5.

Kruse, J. P. and Gu, W. (2009). Modes of p53 regulation. Cell, Vol. 137, No. 4.

Lahav, G. (2008). Oscillations by the p53-Mdm2 feedback loop. Cellular Oscillatory Mechanisms. Springer.

Lähdesmäki, H., Shmulevich, I., and Yli-Harja, O. (2003). On learning gene regulatory networks under the Boolean network model. Machine learning, Vol. 52, No. 1.

Leenders, G., and Tuszynski, J. (2013). Stochastic and deterministic models of cellular p53 regulation. Frontiers in Oncology, Vol. 34.

Levine, M. and Davidson, E. (2005). Gene regulatory networks for development. Proceedings of the National Academy of Sciences, Vol. 102, No. 14.

Li, X., Wang, X., and Chen, G. (2004). Pinning a complex dynamical network to its equilibrium. IEEE Transactions on Circuits and Systems I, Vol. 51, No. 10.

Liu, F., Zhang, S., Guo, W., Wei, Z. G., and Chen, L. (2016). Inference of gene regulatory network based on local bayesian networks. PLoS Computational B., Vol. 12, No. 8.

Liu, Y., Slotine, J., and Barabási, A. (2011). Controllability of complex networks. Nature, Vol. 473, No. 7346.

Mestl, T., Plahte, E., and Omholt, S. (1995). A mathematical framework for describing and analysing gene regulatory networks. Journal of Theoretical Biology, Vol. 176, No. 2.

Momand, J., Jung, D., Wilczynski, S., and Niland, J. (1998). The MDM2 gene amplification database. Nucleic acids research, Vol. 26, No. 15.

Pant, V., Xiong, S., Jackson, J., Post, S., Abbas, H., Quintás-Cardama, A., ... and Lozano, G. (2013). The p53–Mdm2 feedback loop protects against DNA damage by inhibiting p53 activity but is dispensable for p53 stability, development, and longevity. Genes & Development, Vol. 27, No. 17.

Peter, I. and Davidson, E. (2015). Genomic control process: development and evolution. Academic Press.

Perrin, B., Ralaivola, L., Mazurie, A., Mallet, J., and d’Alche–Buc, F. (2003). Gene networks inference using dynamic Bayesian networks. Bioinformatics, Vol. 19, No. 2.

Ryan, K. (2011). p53 and autophagy in cancer: guardian of the genome meets guardian of the proteome. European Journal of Cancer, Vol. 47, No. 1.

Schlitt, T., and Brazma, A. (2007). Current approaches to gene regulatory network modelling. Bioinformatics, Vol. 8, No. 6.

Schon, O., Friedler, A., Bycroft, M., Freund, S. M., and Fersht, A. (2002). Molecular mechanism of the interaction between MDM2 and p53. Journal of Molecular Biology, Vol. 323, No. 3.

Shangary, S., and Wang, S. (2008). Targeting the MDM2-p53 interaction for cancer therapy. Clinical Cancer Research, Vol. 14, No. 17.

Shmulevich, I., Dougherty, E., and Zhang, W. (2002). From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proceedings of the IEEE, Vol. 90, No. 11.

Shmulevich, I., Dougherty, E., Kim, S., and Zhang, W. (2002). Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, Vol. 18, No. 2.

Song, Q. and Cao, J. (2010). On pinning synchronization of directed and undirected complex dynamical networks. IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 57, No. 3.

Strogatz, S. (2001). Exploring complex networks. Nature, Vol. 410, No. 6825.

Su, H., and Wang, X. (2013). Pinning control of complex networked systems: Synchronization, consensus and flocking of networked systems via pinning. Springer Science & Business Media.

Xiang, J. and Chen, G. (2007). On the V-stability of complex dynamical networks. Automatica, Vol. 43, No. 6.

Xu, R., Venayagamoorthy, G., and Wunsch II, D. (2007). Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization. Neural Networks, Vol. 20, No. 8.

Yang, X., Cao, J., and Lu, J. (2012). Stochastic synchronization of complex networks with nonidentical nodes via hybrid adaptive and impulsive control. IEEE Transactions on Circuits and Systems. Vol. 59, No. 2.

Yu, W., DeLellis, P., Chen, G., Di Bernardo, M., and Kurths, J. (2012). Distributed adaptive control of synchronization in complex networks. IEEE Transactions on Automatic Control, Vol. 57, No. 8.

Wang, L., Wang, Z., Han, Q., and Wei, G. (2017). Synchronization control for a class of discrete-time dynamical networks with packet dropouts: A coding-decoding-based approach. IEEE Transactions on Cybernetics.

Wang, R., Saadatpour, A., and Albert, R. (2012). Boolean modeling in systems biology: an overview of methodology and applications. Physical Biology, Vol. 9, No. 5.

Wang, S., Zhao, Y., Aguilar, A., Bernard, D., and Yang, C. (2017). Targeting the MDM2–p53 protein–protein interaction for new cancer therapy: progress and challenges. Cold Spring Harbor Perspectives in Medicine.

Wang, X. F. and Chen, G. (2002). Pinning control of scale-free dynamical networks. Physica A: Statistical Mechanics and its Applications, Vol. 310, No. 3-4.

Wu, Y., Lu, R., Shi, P., Su, H., and Wu, Z. (2018). Sampled-Data Synchronization of Complex Networks with Partial Couplings and T–S Fuzzy Nodes. IEEE Transactions on Fuzzy Systems, Vol.26, No. 2.

Zhang, C., Liu, J., Wang, X., and Feng, Z. (2015). The regulation of the p53/MDM2 feedback loop by microRNAs. RNA & disease, Vol. 2, No. 1.

Zhou, J., Lu, J. A., and Lü, J. (2008). Pinning adaptive synchronization of a general complex dynamical network. Automatica, Vol. 44, No. 4.