Oregonador Modificado: un Enfoque desde la Teoría de Redes Complejas

Autores/as

DOI:

https://doi.org/10.17488/RMIB.41.3.1

Palabras clave:

Biología de sistemas, Reacción BZ, Redes Complejas, Aprendizaje supervisado, Campo angular gramiano

Resumen

En el marco de la Biología de sistemas, se propone en el presente trabajo a la teoría de redes complejas como una herramienta fundamental para la determinación de las variables dinámicas más importantes en mecanismos bioquímicos complejos. Se emplea como modelo de estudio la reacción de Belousov-Zhabotinsky y se plantea como una red compleja bipartita. Mediante la determinación de la propiedad estructural autoridad, se determinan las variables dinámicas con mayor relevancia y se obtiene un modelo matemático de la reacción de Belousov-Zhabotinsky. Se realizó el acoplamiento bidireccional del modelo planteado con otros modelos asociados a procesos biológicos, encontrándose fenómenos de sincronización al variar el parámetro de acoplamiento. Las series de tiempo obtenidas de la solución numérica de los modelos acoplados se emplearon para construir sus respectivas imágenes mediante la técnica de campo angular gramiano. Finalmente, se propone una herramienta de aprendizaje supervisado para la clasificación del tipo de acoplamiento mediante el análisis de las imágenes, obteniéndose porcentajes de exactitud por encima del 94%. La metodología propuesta en el presente trabajo podría extenderse y trasladarse al campo experimental con la finalidad de determinar anomalías en el acoplamiento y sincronización de distintos osciladores fisiológicos.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Draghici, S., Khatri, P., Tarca, A., Amin, K., Done, A., Voichita, C., Georgescu, C. and Romero, R. A systems biology approach for pathway level analysis. Genome Research. 2007;17(10):1537-1545. https://doi.org/10.1101/gr.6202607

Chuang HY, Hofree M, Ideker T. A Decade of Systems Biology. Annual Review of Cell and Developmental Biology. 2010 Oct;26(1):721–44. https://doi.org/10.1146/annurev-cellbio-100109-104122

Kell DB. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discovery Today. 2006;11(23-24):1085–92. https://doi.org/10.1016/j.drudis.2006.10.004

Emmert-Streib F, Dehmer M. Networks for systems biology: conceptual connection of data and function. IET Systems Biology. 2011 Jan;5(3):185–207. https://doi.org/10.1049/iet-syb.2010.0025

Maeda YT, Sano M. Regulatory Dynamics of Synthetic Gene Networks with Positive Feedback. Journal of Molecular Biology. 2006;359(4):1107–24. https://doi.org/10.1016/j.jmb.2006.03.064

Ferrell JE. Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Current Opinion in Cell Biology. 2002;14(2):140–8. https://doi.org/10.1016/S0955-0674(02)00314-9

Mullur R, Liu Y-Y, Brent G. Thyroid Hormone Regulation of Metabolism. Physiological Reviews. 2014 Apr;94(2):355-382. https://doi.org/10.1152/physrev.00030.2013

Porte D, Baskin DG, Schwartz MW. Leptin and Insulin Action in the Central Nervous System. Nutrition Reviews. 2002;60:S20-S29. https://doi.org/10.1301/002966402320634797

Chen WW, Niepel M, Sorger PK. Classic and contemporary approaches to modeling biochemical reactions. Genes & Development. 2010;24(17):1861–75. https://doi.org/10.1101/gad.1945410

Alves R, Antunes F, Salvador A. Tools for kinetic modeling of biochemical networks. Nature Biotechnology. 2006;24(6):667–672. https://doi.org/10.1038/nbt0606-667

Radulescu O, Gorban AN, Zinovyev A, Noel V. Reduction of dynamical biochemical reactions networks in computational biology. Frontiers in Genetics. 2012;3:131. https://dx.doi.org/10.3389%2Ffgene.2012.00131

Zhabotinsky AM. A history of chemical oscillations and waves. Chaos: An Interdisciplinary. Journal of Nonlinear Science. 1991;1(4):379–86. https://doi.org/10.1063/1.165848

Sagues F, Epstein IR. Nonlinear Chemical Dynamics. Dalton Transactions. 2003;32(7):1201-1217. https://doi.org/10.1039/B210932H

Epstein IR, Pojman JA, Steinbock O. Introduction: Self-organization in nonequilibrium chemical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2006;16(3):037101. https://doi.org/10.1063/1.2354477

Noyes RM, Field R, Koros E. Oscillations in chemical systems. I. Detailed mechanism in a system showing temporal oscillations. Journal of the American Chemical Society. 1972;94(4):1394–5. https://doi.org/10.1021/ja00759a080

Field RJ, Koros E, Noyes RM. Oscillations in chemical systems. II. Thorough analysis of temporal oscillation in the bromate-cerium-malonic acid system. Journal of the American Chemical Society. 1972;94(25):8649–64. https://doi.org/10.1021/ja00780a001

Shanks, N. Modeling biological systems: the Belousov–Zhabotinsky reaction. Foundations of Chemistry. 2011; 3(1): 33-53.

Field RJ, Noyes RM. Oscillations in chemical systems. IV. Limit cycle behavior in a model of a real chemical reaction. The Journal of Chemical Physics. 1974;60(5):1877–84. https://doi.org/10.1063/1.1681288

Györgyi L, Turanyi T, Field RJ. Mechanistic details of the oscillatory Belousov-Zhabotinskii reaction. The Journal of Physical Chemistry. 1990;94(18):7162–70. https://doi.org/10.1021/j100381a039

Sayama H. Introduction to the modeling and analysis of complex systems. New York: Open SUNY Textbooks, Milne Library, State University of New York at Geneseo; 2015. 478p.

Lesne A. Complex Networks: from Graph Theory to Biology. Letters in Mathematical Physics. 2006;78(3):235–62. https://doi.org/10.1007/s11005-006-0123-1

Mason O, Verwoerd M. Graph theory and networks in Biology. IET Systems Biology. 2007;1(2):89–119. https://doi.org/10.1049/iet-syb:20060038

Albert R, Barabási A-L. Statistical mechanics of complex networks. Reviews of Modern Physics. 2002;74(1):47–97. https://doi.org/10.1103/RevModPhys.74.47

Costa LDF, Rodrigues FA, Hilgetag CC, Kaiser M. Beyond the average: Detecting global singular nodes from local features in complex networks. EPL (Europhysics Letters). 2009;87(1):18008. https://doi.org/10.1209/0295-5075/87/18008

Loscalzo J, Barabási Albert-László, Silverman EK. Network medicine: complex systems in human disease and therapeutics. Cambridge, MA: Harvard University Press; 2017. 448p.

Costa LDF, Rodrigues FA, Cristino AS. Complex networks: the key to systems biology. Genetics and Molecular Biology. 2008;31(3):591–601. http://dx.doi.org/10.1590/S1415-47572008000400001

Hornberg J, Bruggeman FJ, Westerhoff HV, Lankelma J. Cancer: A Systems Biology disease. Biosystems. 2006;83(2-3):81-90. https://doi.org/10.1016/j.biosystems.2005.05.014

Cardon LR, Bell JI. Association study designs for complex diseases. Nature Reviews Genetics. 2001;2(2):91-99. https://doi.org/10.1038/35052543

DeFronzo RA. Insulin Resistance, Hyperinsulinemia, and Coronary Artery Disease: A Complex Metabolic Web. Journal of Cardiovascular Pharmacology. 1992;20 (Suppl 1):S1-S16. https://doi.org/10.1097/00005344-199200111-00002

Karalliedde J, Gnudi L. Diabetes mellitus, a complex and heterogeneous disease, and the role of insulin resistance as a determinant of diabetic kidney disease. Nephrology Dialysis Transplantation. 2016:31(2):206-13. https://doi.org/10.1093/ndt/gfu405

Upadhyay SK. Chemical kinetics and reaction dynamics. New Delhi: Springer Netherlands; 2006. 256p. https://doi.org/10.1007/978-1-4020-4547-9

Kotsiantis SB, Zaharakis ID, Pintelas PE. Machine learning: a review of classification and combining techniques. Artificial Intelligence Review. 2006;26:159–90. https://doi.org/10.1007/s10462-007-9052-3

Berzal, F. Redes neuronales & deep learning. 1ra. ed. Granada, España: Edición Independiente; 2018. 753p.

Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. 2009;361-362.

Moon FC. Chaotic and fractal dynamics: introduction for applied scientists and engineers. 2nd. ed. New York: Wiley; 1992. 528p.

Afraimovich VS, Lin WW, Rulkov NF. Fractal dimension for poincaré recurrences as an indicator of synchronized chaotic regimes. International Journal of Bifurcation and Chaos. 2000;10(10):2323-2337. https://doi.org/10.1142/S0218127400001456

Liu Z, Lai Y, Matías M. Universal scaling of Lyapunov exponents in coupled chaotic oscillators. Physical Review E. 2003;67(4): 045203. http://dx.doi.org/10.1103/PhysRevE.67.045203

Porta A, Baselli G, Lombardi F, Montano N, Malliani A, Cerutti S. Conditional entropy approach for the evaluation of the coupling strength. Biological Cybernetics. 1999;81:119-129. https://doi.org/10.1007/s004220050549

Eckmann, JP, Kamphorst SO, Ruelle D. Recurrence plots of dynamical systems. Europhysics Letter. 1987; 4(9):973-977. https://doi.org/10.1142/9789812833709_0030

Hernández Sánchez S, Fernández Pozo R, Hernández Gómez LA. Deep Neural Networks for Driver Identification Using Accelerometer Signals from Smartphones. In: Abramowicz W, Corchuelo R. (eds) Business Information Systems. Lecture Notes in Business Information Processing. Switzerland: Springer, Cham; 2019, p. 206-220. https://doi.org/10.1007/978-3-030-20482-2_17

Bode BW. Clinical Utility of the Continuous Glucose Monitoring System. Diabetes Technology & Therapeutics. 2000;2(Suppl 1):S35-41. https://doi.org/10.1089/15209150050214104

Yoo EH, Lee SY. Glucose Biosensors: An Overview of Use in Clinical Practice. Sensors. 2010;10(5):4558-4576. https://doi.org/10.3390/s100504558

Frost MC, Meyerhoff ME. Implantable chemical sensors for real-time clinical monitoring: progress and challenges. Current Opinion in Chemical Biology. 2002;6(5):633-641. https://doi.org/10.1016/s1367-5931(02)00371-x

Frost MC, Meyerhoff ME. Real-Time Monitoring of Critical Care Analytes in the Bloodstream with Chemical Sensors: Progress and Challenges. Annual Review of Analytical Chemistry. 2015;8(1):171-192. https://doi.org/10.1146/annurev-anchem-071114-040443

Coveney PV, Fowler PW. Modelling biological complexity: a physical scientist's perspective. Journal of The Royal Society Interface. 2005;2(4):267-280. https://doi.org/10.1098/rsif.2005.0045

Kleinberg JM. Authoritative sources in a hyperlinked environment. Journal of the ACM. 1999;46(5): 604-632. https://doi.org/10.1145/324133.324140

Aton SJ, Herzog ED. Come Together, Right…Now: Synchronization of Rhythms in a Mammalian Circadian Clock. Neuron. 2005;48(4):531–534. https://dx.doi.org/10.1016%2Fj.neuron.2005.11.001

Lorenzo González MN. Influencia del Ruido Gaussiano Correlacionado en la Sincronización de Sistemas Caóticos [Ph.D.'s thesis]. [Santiago de Compostela]:Universidad de Santiago de Compostela, 2000.161p. Spanish.

Lefever R, Nicolis G, Borckmans P. The brusselator: it does oscillate all the same. Journal of the Chemical Society, Faraday Transactions 1: Physical Chemistry in Condensed Phases. 1988;84(4):1013-1023. https://doi.org/10.1039/F19888401013

Vaidyanathan, S. Anti-synchronization of Brusselator chemical reaction systems via adaptive control. International Journal of ChemTech Research. 2015;8(6):759-68.

Sel'kov EE. Self-Oscillations in glycolysis. 1. A Simple Kinetic Model. European Journal of Biochemistry. 1968;4(1):79–86. https://doi.org/10.1111/j.1432-1033.1968.tb00175.x

Keener JP, Sneyd J. Mathematical physiology. 2nd. Ed. New York: Springer; 2009. 470p. https://doi.org/10.1007/978-0-387-75847-3

Butcher J. Numerical methods for ordinary differential equations in the 20th century. In: Brezinski C, Wuytack, L. (eds). Numerical Analysis: Historical Developments in the 20th Century. Amsterdam: Elsevier; 2001.449–77p. https://doi.org/10.1016/B978-0-444-50617-7.50018-5

Faouzi J, Janati H. pyts: A python package for time series classification. Journal of Machine Learning Research. 2020; 21(46):1−6.

Wang Z, Oates T. Imaging time-series to improve classification and imputation. Proceedings of the 24th International Joint Conference on Artificial Intelligence. 2015;3939-45.

Брагин, АB, Bragin A, Спицын ВГ, Spicyn, V.Electroencephalogram Analysis Based on Gramian Angular Field Transformation. Proceedings of the 29th International Conference on Computer Graphics and Vision. 2019;2485:273-75. https://doi.org/10.30987/graphicon-2019-2-273-275

Damaševičius R, Maskeliūnas R, Woźniak M, Polap D. Visualization of physiologic signals based on Hjorth parameters and Gramian Angular Fields. 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI). 2018; 91-6. https://doi.org/10.1109/sami.2018.8323992

Qin Z, Zhang Y, Meng S, Qin Z, Choo K-KR. Imaging and fusing time series for wearable sensor-based human activity recognition. Information Fusion. 2020;53:80–7. https://doi.org/10.1016/j.inffus.2019.06.014

Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B. Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research. 2013; 14(35): 2349−2353.

Demšar J, Zupan B. Orange: Data Mining Fruitful and Fun- A Hystorical Perspective. Informatica 2013;37:55-60.

Godec P, Pančur M, Ilenič N, Čopar A, Stražar M, Erjavec A, Pretnar A, Demšar J, Starič A, Toplak M, Žagar L, Hartman J, Wang H, Bellazzi R, Petrovič U, Garagna S, Zuccotti M, Park D, Shaulsky G, Zupan B. Democratized image analytics by visual programming through integration of deep models and small-scale machine learning. Nature Communications. 2019;10(1):4551. https://doi.org/10.1038/s41467-019-12397-x

Weiss K, Khoshgoftaar T, Wang D. A survey of transfer learning. Journal of Big Data. 2016;3(1):1345–1359. https://doi.org/10.1186/s40537-016-0043-6

Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In Kůrková V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I. International conference on artificial neural networks. Rhodes: Springer International Publishing. Cham; 2018. p. 270-279. https://doi.org/10.1007/978-3-030-01424-7_27

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas: IEEE; 2016. 2818-2826p. https://doi.org/10.1109/CVPR.2016.308

Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size [Internet]. arXiv.org; 2020. Available from: https://arxiv.org/abs/1602.07360

Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [Internet]. arXiv.org; 2020. Available from: https://arxiv.org/abs/1409.1556v6

Almagro Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O. DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics. 2017;33(21):3387-3395. https://doi.org/10.1093/bioinformatics/btx431

Müller AC, Guido S. Introduction to machine learning with Python a guide for data scientists. Beijing: OReilly; 2018. p. 251-303.

Hawkins DM. The Problem of Overfitting. Journal of Chemical Information and Computer Sciences. 2004;44(1):1-12. https://doi.org/10.1021/ci0342472

Sokolova M, Japkowicz N, Szpakowicz S. Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In Sattar A, Kang B. (eds). AI 2006: Advances in Artificial Intelligence. Berlin: Springer; 2006. 1015–21p. https://doi.org/10.1007/11941439_114

Pinto CMA, Mendes Lopes A, Tenreiro Machado JA. A review of power laws in real life phenomena. Communications in Nonlinear Science and Numerical Simulation. 2012;17(9):3558–78. https://doi.org/10.1016/j.cnsns.2012.01.013

Gisiger T. Scale invariance in biology: coincidence or footprint of a universal mechanism? Biological Reviews of the Cambridge Philosophical Society. 2001;76(2):161–209. https://doi.org/10.1017/s1464793101005607

Amemiya T, Kádár S, Kettunen P, Showalter K. Spiral Wave Formation in Three-Dimensional Excitable Media. Physical Review Letters. 1996;77(15):3244–7. https://doi.org/10.1103/physrevlett.77.3244

Krug HJ, Pohlmann L, Kuhnert L. Analysis of the modified complete Oregonator accounting for oxygen sensitivity and photosensitivity of Belousov-Zhabotinskii systems. The Journal of Physical Chemistry. 1990;94(12):4862–6. https://doi.org/10.1021/j100375a021

Ma J, Li F, Huang L, Jin W-Y. Complete synchronization, phase synchronization and parameters estimation in a realistic chaotic system. Communications in Nonlinear Science and Numerical Simulation. 2011;16(9):3770–3785. https://doi.org/10.1016/j.cnsns.2010.12.030

Rajesh S, Sinha S, Sinha S. Synchronization in coupled cells with activator-inhibitor pathways. Physical Review E. 2007;75(1): 011906. https://doi.org/10.1103/physreve.75.011906

Belykh I, Lange ED, Hasler M. Synchronization of Bursting Neurons: What Matters in the Network Topology. Physical Review Letters. 2005;94(18):1881011-1881014. https://doi.org/10.1103/physrevlett.94.188101

Belhaq M, Houssni, M. Quasi-periodic oscillations, chaos and suppression of chaos in a nonlinear oscillator driven by parametric and external excitations. Nonlinear Dynamics. 1999;18(1):1-24. https://doi.org/10.1023/A%3A1008315706651

Ng A. Feature selection, L 1 vs. L 2 regularization, and rotational invariance. In Brodley C (ed.). ICML '04: Proceedings of the twenty-first international conference on Machine learning. New York: Association for Computing Machinery; 2004. 78-86p. https://doi.org/10.1145/1015330.1015435

Ng A. Preventing" overfitting" of cross-validation data. In Fisher DH (ed.). ICML '97: Proceedings of the Fourteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc; 1997. 245-253p.

Mao W, Mu X, Zheng Y, Yan G. Leave-one-out cross-validation-based model selection for multi-input multi-output support vector machine. Neural Computing and Applications. 2012;24(2):441-451. https://doi.org/10.1007/s00521-012-1234-5

Grünauer A, Vincze M. Using Dimension Reduction to Improve the Classification of High-dimensional Data [Internet]. OAGM Workshop; 2020. Available from: https://arxiv.org/pdf/1505.06907.pdf

Azure Machine Learning. Evitar el sobreajuste y los datos desequilibrados con el aprendizaje automático automatizado. Microsoft [Internet]. 2019. Available from: https://docs.microsoft.com/es-es/azure/machine-learning/concept-manage-ml-pitfalls

Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Calster BV. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology. 2019;110:12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004

Rajagopal S, Hareesha KS, Kundapur PP. Performance analysis of binary and multiclass models using azure machine learning. International Journal of Electrical and Computer Engineering (IJECE). 2020;10(1):978-986. http://doi.org/10.11591/ijece.v10i1.pp978-986

Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI'95: Proceedings of the 14th international joint conference on Artificial intelligence. 1995;14(2):1137-1145

Alducin Castillo J, Yanez Suárez O. Brust Carmona H. Electroencephalographic analysis of the functional conectivity in habituation by graphics theory. Revista Mexicana de Ingeniería Biomédica. 2016;37(3):181-200. https://doi.org/10.17488/RMIB.37.3.3

Glass L. Synchronization and rhythmic processes in physiology. Nature. 2001;410(6825):277–84. https://doi.org/10.1038/35065745

Descargas

Publicado

2020-09-01

Cómo citar

Rojas, J. F., Arzola, J. A., & Vidal Robles, E. (2020). Oregonador Modificado: un Enfoque desde la Teoría de Redes Complejas. Revista Mexicana De Ingenieria Biomedica, 41(3), 6–27. https://doi.org/10.17488/RMIB.41.3.1

Número

Sección

Artículos de Investigación

Citas Dimensions

Artículos similares

También puede {advancedSearchLink} para este artículo.