Abstract
Bayesian networks are widely used to infer genes regulatory network from their transcriptional expression data. Bayesian network of the best score is usually chosen as genes regulatory model. However, without the hint from biological ground truth, and given a small number of transcriptional expression observations, the resulting Bayesian networks might not correspond to the real one. To deal with these two constrains, this paper proposes a stochastic approach to fit an existing hypothetical gene regulatory network, derived from biological evidence, with few available amount of transcriptional expression levels of the genes. The hypothetical gene regulatory network is set as an initial model of Bayesian network and fitted with transcriptional expression data by using Metropolis-Hastings algorithm. In this work, the transcriptional regulation of gene CYC1 by co-regulators HAP2 HAP3 HAP4 of yeast (Saccharomyces Cerevisiae) is considered as example. Due to the simulation results, ten probable gene regulatory networks which are similar to the given hypothetical model are obtained. This shows that Metropolis-Hastings algorithm can be used as a simulation model for gene regulatory network.
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References
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Molecular Biology of the Cell, 4th edn. Garland Science, New York (2002)
Derisi, J.L., Iyer, V.R., Brown, P.O.: Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science, Sciencemag 278, 680–686 (1997)
Wu, X., Ye, Y., Subramanian, K.R.: Interactive Analysis of Gene Interactions Using Graphical Gaussian Model. In: BIOKDD 2003: 3rd ACM SIGKDD Workshop on Data Mining in Bioinformatics, pp. 63–69 (2003)
Shmulevith, I., Dougherty, E.R., Zhang, W.: From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks. Proceedings of the IEEE 90(11), 1778–1792 (2002)
Hartemink, A.J., Gifford, D.K., Jaakkola, T.S., Young, R.A.: Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. Pac. Symp. Biocomputing, Hawaii, pp. 422–433 (January 2001)
Husmeier, D.: Sensitivity and Specificity of Inferring Genetic Regulatory Interactions from Microarray Experiments with Dynamic Bayesian Networks. In: Bioinformatics, vol. 19(17), pp. 2271–2282. Oxford, England (2003)
Hortner, H., Ammerer, G., Hartter, E., Hamilton, B., Rytka, J., Bilinski, T., Ruis, H.: Regulation of Synthesis of Catalases and iso-1-cytochrome c in Saccharomyces Cerevisiae by Glucose, Oxygen and Heme. Eur. J. Biochem. 128(1), 179–184 (1982)
Olesen, J.T., Guarente, L.: The HAP2 Subunit of Yeast CCAAT Transcriptional Activator Contains Adjacent Domains for Subunit Association and DNA Recognition: Model for the HAP 2/3/4 Complex. Genes Dev. 4, 1714–1729 (1990)
Gilk, W.R., Richardson, S., Spiegelhalter, D.: Markov Chain Monte Carlo in Practice. Chapman & Hall, London (1996)
Milton, J.S., Tsokos, J.O.: Statistical Methods in the Biological and Health Sciences, pp. 123–131. McGraw-Hill, Japan (1983)
Balding, D.J., Bishop, M., Cannings, C.: Handbook of Statistical Genetics, p. 730. Wiley, England (2001)
Gamerman, D.: Markov Chain Monte Carlo Stochastic Simulation for Bayesian Inference. Chapman & Hall, London (1997)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, pp. 348–354. Academic Press, San Francisco (2001)
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Kirimasthong, K., Manorat, A., Chaijaruwanich, J., Prasitwattanaseree, S., Thammarongtham, C. (2007). Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_26
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DOI: https://doi.org/10.1007/978-3-540-73871-8_26
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