Abstract
P38 MAPK is one of the most important central regulatory proteins that can respond extra environmental stresses. It can activate or inhibit many other genes, which can lead some disease, such as cancers or inflammations etc. We proposed a new differential equation model using linear regression analysis to calculate the weight values of the Genetic Regulatory Networks to simulate the P38 MAPK pathway in Genetic level. The results of the network are reasonable. We can investigate the P38 MAPK pathway some extra hypotheses from the result model, and provide biologists optimal designs for further experiments of disease researches.
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References
Zhang, J., Salojin, K.V., Gao, J.X., et al.: p38 mitogen-activated protein kinase mediates signal integration of TCR/CD28 costimulation in primary murine T cells. The Journal of Immunology 162, 3819–3829 (1999)
Abe, J.I., Kusuhara, M., Richard, J., et al.: Big Mitogen-activated Protein Kinase 1 (BMK1) Is a Redox-sensitive Kinase. The Journal of Biological Chemistry 271(28), 16586–16589 (1996)
Galcheva-Gargova, Z., Derijard, B., Wu, I.H., et al.: An osmosensing signal transduction pathway in mammalian cells. Science 265, 806–808 (1994)
Davis, R.J.: The mitogen-activated protein kinase signal transduction pathway. The Journal of Biological Chemistry 286, 14553–14556 (1993)
Han, J., Lee, J.D., Bibbs, L., et al.: A MAP kinase targeted by endotoxin and hyperosmolarity in mammalian cells. Science 265, 808–811 (1994)
Nick, J.A., Young, S.K., Brown, K.K., et al.: Role of p38 Mitogen-Activated Protein Kinase in a Murine Model of Pulmonary Inflammation. The Journal of Immunology 164, 2151–2159 (2000)
Bradham, C., McClay, D.R.: p38 MAPK in development and cancer. Cell Cycle 5(8), 824–828 (2006)
Drulhe, S., Ferrari-Trecate, G., de Jong, H.: The Switching Threshold Reconstruction Problem for Piecewise-Affine Models of Genetic Regulatory Networks. IEEE Transactions on Circuits And Systems I-Regular Papers 29, 153–165 (2008)
Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pacific Symposium on Biocomputing 4, 17–28 (1999)
Pe’er, D., Nachman, I., Linial, M., et al.: Using bayesian networks to analyze expression data. J.Comput. Biol. 7, 601–620 (2000)
Steuer, R., Kurths, J., Daub, C.O., et al.: The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18(suppl. 2), 231–240 (2002)
D’haeseleer, P., Liang, S., Somogyi, R.: Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16, 707–726 (2000)
Wuensche, A.: Discrete Dynamics Lab (DDLab), http://www.ddlab.com/
Yu, J., Smith, V.A., Wang, P.P., et al.: Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 3594–3603 (2004)
Basso, K., Margolin, A.A., Stolovitzky, G., et al.: Reverse engineering of regulatory networks in human B. Cells 37, 382–390 (2005)
di Bernardo, D., Thompson, M., Gardner, T., et al.: Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23, 377–383 (2005)
Hucka, M., Finney, A., Bornstein, B.J., et al.: Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML). project. IEE Systems Biology 1(1), 41–53 (2004)
Shannon, P., Markiel, A., Ozier, W., et al.: Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13, 2498–2504 (2003)
Monga, M., Sausville, E.A.: Developmental Therapeutics Program at the NCI: molecular target and drug discovery process. Nature 2, 0887–6924 (2002)
The Novartis data U95, http://dtpsearch.ncifcrf.gov/FTP/WEB_DATA_NOVARTIS.ZIP
The data used in this paper, http://uploadingit.com/files/download/1075417_srm3y/Data.xls
The P38 MAPK provided by BioCarta, http://www.biocarta.com/pathfiles/h_P38MAPKPATHWAY.asp
Krizman, D.B., Wagner, L., Lash, A., et al.: The Cancer Genome Anatomy Project: EST Sequencing and the Genetics of Cancer Progression. Neoplasia 1, 101–106 (1999)
Samaranayake, M., Ji, H., Ainscough, J.: Force directed graph drawing algorithms for Macro cell placement. Lecture Notes in Engineering and Computer Science, vol. S I-III, pp. 222–227 (2008)
Kendal, J.R., Rendell, L., Pike, T.W., et al.: Nine-spined sticklebacks deploy a hill-climbing social learning strategy. Behavioral Ecology 20, 238–244 (2009)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Researich 3, 1157–1182 (2003)
Holt, J.S.: Data analysis with SPSS: A first course in applied statistics. Teaching Sociology 36, 285–287 (2008)
The code of this paper, http://uploadingit.com/files/1076574_i3moj/Codes.zip
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Zheng, M., Liu, GX., Wang, H., Zhou, CG. (2009). Gene Regulatory Network Reconstruction of P38 MAPK Pathway Using Ordinary Differential Equation with Linear Regression Analysis. In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_30
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DOI: https://doi.org/10.1007/978-3-642-03156-4_30
Publisher Name: Springer, Berlin, Heidelberg
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