Although limitations exist, current research is making headway all the time and providing future research directions as it does so.
References
Berger, James O (1985). Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (Second ed.). Springer-Verlag.
Friedman, N., Linial, M., Nachman, I., and Pe'er, D. (2000). Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601 -- 620.
Hartemink a.J., Gifford D.K., Jaakkola T.S., Young, R.A.. (2001). Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Biocomput, World Scientific Publishing Co.
Howson, C.; Urbach, P. (2005). Scientific Reasoning: the Bayesian Approach (3rd ed.). Open Court Publishing Company.
Jensen, F. (1996). An introduction...
Berlin: Springer.
Jong, H.D. (2002). Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal of Computational Biology, 9, 67 -- 103
Murphy, K., and Mian, S. (1999). Modeling gene expression data using dynamic Bayesian networks. Technical report, University of California at Berkeley, Berkeley, CA.
Pe'er, D., Regev, a., Elidan, G., and Friedman, N. (2001). Inferring sub-networks from perturbed expression. Bioinformatics 17, S215 -- S224.
Roy, C., Roy, B., & Udin, M. (2007). Bayesian Approaches to Modeling Gene Regulatory Networks: A Literature Review, Bayesian, 9, 23-25
Zou M, & Conzen S. (2005). A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics.
