Modelo generado computacionalmente de interacción genética del próximo estado basado en Arabidopsis thaliana
Resumen
La elaboración de modelos de interacción genética debe ser un esfuerzo totalmente intencional y colaborativo. Todos los aspectos de la investigación, tales como el cultivo de las plantas, la obtención de las mediciones, el refinamiento de los datos recopilados, el desarrollo del marco estadístico, y la formulación y aplicación de técnicas algorítmicas, deben colaborar entre sí para establecer prácticas reproducibles y eficaces. Este artículo se centra, de manera holística, en el proceso de creación de modelos de interacción genética basados en los datos de la abundancia de transcritos obtenidos de la estimulación de la planta Arabidopsis thaliana mediante hormonas vegetales.
Descargas
Citas
Allen, E. E., Fetrow, J. S., Daniel, L. W., Thomas, S. J., & John, D. J. (2006, January). Algebraic dependency models of protein signal transduction networks from time-series data. Journal of Theoretical Biology, 238(2), 317-330. DOI: 10.1016/j.jtbi.2005.05.010
Cao, J., Qi, X., & Zhao, H. (2012). Modeling gene regulation networks using ordinary differential equations. In Next generation microarray bioinformatics, 802, 185-197. Springer. DOI:10.1007/978-1-61779-400-1_12
De la Fuente, A., Bing, N., Hoeschele, I., & Mendes, P. (2004). Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics, 20(18), 3565-3574.
Harkey, A. F., Watkins, J. M., Olex, A. L., DiNapoli, K. T., Lewis, D. R., Fetrow, J. S., .Muday, G. K. (2018). Identification of transcriptional and receptor networks that control root responses to ethylene. Plant Physiology, 176(3), 2095-2118. Retrieved from http://www.plantphysiol.org/content/176/3/2095. DOI: 10.1104/pp.17.00907
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E.I. George, and a rejoinder by the authors). Statistical Science, 14(4), 382-417.
John, D. J., Fetrow, J. S., & Norris, J. L. (2011, September/October). Continuous cotemporal probabilistic modeling of systems biology networks from sparse data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(5), 1208-1222. DOI:10.1109/TCBB.2010.95
Krämer, N., Schäfer, J., & Boulesteix, A.-L. (2009). Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics, 10(384). DOI:10.1186/1471-2105-10-384
LaPointe, B. A. (2017). Arabidopsis thaliana gene interaction exploration with CHC genetic algorithm (Unpublished master’s thesis). Wake Forest University, Department of Computer Science.
LaPointe, B. A., John, D. J., Norris, J. L., Harkey, A. F., Muhlemann, J. K., & Muday, G. K. (submitted). A specialized genetic algorithm to model cotemporal hierarchical Arabidopsis thaliana gene interactions.
Laubenbacher, R., & Stigler, B. (2004, August). A computational algebra approach to the reverse engineering of gene regulatory networks. Journal of Theoretical Biology, 229(4), 523-537. DOI: 10.1016/j. jbi2004.04.037
Lewis, D. R., Olex, A. L., Lundy, S. R., Turkett, W. H., Fetrow, J. S., & Muday, G. K. (2013, September). A kinetic analysis of the Auxin transcriptome reveals cell wall remodeling proteins that modulate lateral root development in Arabidopsis. The Plant Cell, 25, 3329-3346. DOI:10.1105/tpc.113.114868
Li, H., & Gai, J. (2008). Gradient directed regularization for sparse Gaussian, concentration graphs with applications to inference of genetic networks. Biostatistics, 7(2), 302-317.
Liang, J., & Han, J. (2012). Stochastic Boolean networks: an efficient approach to modeling gene regulatory networks. BMC Systems Biology, 6(113), 1-20. Retrieved from http://www.biomedcentral.com/1752-0509/6/113
Moon, J. W. (1970). Counting labelled trees, Canadian mathematical monographs, n. o 1. In Canadian Mathematical Congress, Montreal, Quebec.
Norris, J. L., Patton, K. L., Huang, S., John, D. J., & Muday, G. K. (2015, April). First and second order Markov posterior probabilities on multiple time-course data sets. In SoutheastCon 2015 (pp. 1-8). Norfolk, Virginia: IEEE. DOI: 10.1109/SECON.2015.7132880
O’Malley, R. C., Huang, S. S. C., Song, L., Lewsey, M. G., Bartlett, A., Nery, J. R., ... & Ecker, J. R. (2016). Cistrome and epicistrome features shape the regulatory DNA landscape. Cell, 165(5), 1280-1292.
Patton, K. L. (2012). Bayesian interaction and association networks from multiple replicates of sparse time-course data (Doctoral dissertation, Wake Forest University).
Patton, K. L., John, D. J., & Norris, J. L. (2012, June). Bayesian probabilistic network modeling from multiple independent replicates. BMC Bioinformatics, 13(Supplement 9), 1-13.
Patton, K. L., John, D. J., Norris, J. L., Lewis, D., & Muday, G. (2013). Hierarchical Bayesian system network modeling of multiple related replicates. BMC Bioinformatics, 7, 803-812.
Patton, K. L., John, D. J., Norris, J. L., Lewis, D. R., & Muday, G. K. (2014, March/April). Hierarchical probabilistic interaction modeling for multiple gene expression replicates. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(2), 336-346. DOI: 10.1109/TCBB.2014.2299804
Stigler, B. (2007). Polynomial dynamical systems in system biology. 2006 AMS Proceedings of Symposia in Applied Mathematics, 64, 59-84.
Wille, A., Zimmermann, P., Vranová, E., Fürholz, A., Laule, O., Bleuler, S., ... & Zitzler, E. (2004). Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. Genome biology, 5(11), R92. DOI: 10.1186/gb-2004-5-11-r92
