Modeling of gene regulatory networks: A review

Nedumparambathmarath Vijesh, Swarup Kumar Chakrabarti, Janardanan Sreekumar

Central Tuber Crops Research Institute, Thiruvananthapuram, India.

DOI: 10.4236/jbise.2013.62A027                

Abstract

Gene regulatory networks play an important role the molecular mechanism underlying biological processes. Modeling of these networks is an important challenge to be addressed in the post genomic era. Several methods have been proposed for estimating gene networks from gene expression data. Computational methods for development of network models and analysis of their functionality have proved to be valuable tools in bioinformatics applications. In this paper we tried to review the different methods for reconstructing gene regulatory networks.

Keywords

Gene Network; Gene Expression Data; Gene Regulation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Guy, K. and Ron, S. (2008) Modelling and analysis of gene regulatory networks. www.nature.com/reviews/molcellbio.
[2]	Davidson, E. and Levin, M. (2005) Gene regulatory networks. Proceedings of the National Academy of Sciences of the United States of America, 102, 4935. doi:10.1073/pnas.0502024102
[3]	Hasty, J., McMillen, D., Isaacs, F. and Collins, J.J. (2001) Computational studies of gene regulatory networks: In numero molecular biology. Nature Reviews Genetics, 2, 268-279. doi:10.1038/35066056
[4]	Martin, T.S., Johannes, J.M. and Werner, D. (2010) Comparative study of three commonly used continuous deterministic methods for modeling gene regulation net works. BMC Bioinformatics, 11, 459. doi:10.1186/1471-2105-11-459
[5]	Wessels, L., van Someren, E. and Reinders, M.A. (3-7 January 2001) Comparison of genetic network models. Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 508-519.
[6]	Cho, K.H., Choo, S.M., Jung, S.H., Kim, J.R., Choi, H.S., Kim, J. (2007) Reverse engineering of gene regulatory networks. IET Systems Biology, 1, 149-163. doi:10.1049/iet-syb:20060075
[7]	De Jong, H. (2002) Modeling and simulation of genetic regulatory systems: A literature review. Journal of Com putational Biology, 9, 67-103. doi:10.1089/10665270252833208
[8]	Glass, L. and Kauffman, S.A. (1973) The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 39, 103-129. doi:10.1016/0022-5193(73)90208-7
[9]	Thomas, R. (1973) Boolean formalization of genetic control circuits. Journal of Theoretical Biology, 42, 563-585. doi:10.1016/0022-5193(73)90247-6
[10]	Vladimir, F. (2005) Handbook of computational molecular biology. University of California, Davis.
[11]	Faure, A., Naldi, A., Chaouiya, C. and Thieffry, D. (2006) Dynamical analysis of a generic boolean model for the control of the mammalian cell cycle. Bioinformatics, 22, e124-e131. doi:10.1093/bioinformatics/btl210
[12]	Akutsu, T., Miyano, S. and Kuhara, S. (2000) Inferring quality relations in genetic networks and metabolic path ways. Bioinformatics, 16, 727-734. doi:10.1093/bioinformatics/16.8.727
[13]	Tany, A. and Shamir, R. (2001) Computational expansion of gene networks. Bioinformatics, 17, S270-S278. doi:10.1093/bioinformatics/17.suppl_1.S270
[14]	Lahdesmaki, Shmuleveich, L. and Yli-Harja, O. (2003) On learning gene regulatory networks under the Boolean network model. Machine Learning, 52, 147-167. doi:10.1023/A:1023905711304
[15]	Shmulevich, I., Dougherty, E.R., Kim, S. and Zhang, W. (2002) Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18, 261-274. doi:10.1093/bioinformatics/18.2.261
[16]	Shmulevich, I., Gluhovsky, I., Hashimoto, R.F., Dougherty, E.R. and Zhan, W. (2003) Steady-state analysis of genetic regulatory networks modelled by probabilistic Boolean networks. Comparative and Functional Genomics, 4, 601-608. doi:10.1002/cfg.342
[17]	Pearl, J. (1988) Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Mateo.
[18]	Han, J.W. and Micheline, K. (2007) Data mining: Concepts and techniques. Elsevier Science, New York.
[19]	Friedman, N., Linial, M., Nachman, I. and Pe’er, D. (2000) Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7, 601-620. doi:10.1089/106652700750050961
[20]	Armaanzas, R., Inza, I. and Larraaga, P. (2008) Detecting reliable gene interactions by a hierarchy of Bayesian network classi?ers. Computer Methods and Programs in Biomedicine, 91, 110-121. doi:10.1016/j.cmpb.2008.02.010
[21]	Beal, M.J., Falciani, F., Ghahramani, Z., Rangel, C. and Wild, D.L. (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioin formatics, 21, 349-356. doi:10.1093/bioinformatics/bti014
[22]	Mason, O. and Verwoerd, M. (2007) Graph theory and networks in biology. IET Systems Biology, 1, 89-119. doi:10.1049/iet-syb:20060038
[23]	Sauer, U., et al. (1996) Physiology and metabolic fluxes of wildtype and riboflavin-producing Bacillus subtilis. Applied and Environmental Microbiology, 62, 3687 3696.
[24]	Ness, S.A. (2006) Basic microarray analysis: Strategies for successful experiments. Methods in Molecular Biology, 316, 13-33.
[25]	Kingsmore, S.F. (2006) Multiplexed protein measurement: Technologies and applications of protein and anti body arrays. Nature Reviews Drug Discovery, 5, 310-320. doi:10.1038/nrd2006
[26]	Chen, T., He, H.L. and Church, G.M. (1999) Modeling gene expression with differential equations. Pacific Symposium on Biocomputing, 4, 29-40.
[27]	D’Haeseleer, P., Wen, X., Fuhrman, S. and Somogyi, R. (1999) Linear modeling of mRNA expression levels during CNS development and injury. Pacific Symposium on Biocomputing, 4, 41-52.
[28]	Hellerstein, M.K. (2003) In vivo measurement of fluxes through metabolic pathways: The missing link in functional genomics and pharmaceutical research. Annual Re view of Nutrition, 23, 379-402. doi:10.1146/annurev.nutr.23.011702.073045
[29]	Vohradsky, J. (2001) Neural network model of gene expression. The FASEB Journal, 15, 846-854. doi:10.1096/fj.00-0361com
[30]	Savageau, M.A. (1976) Biochemical systems analysis: A study of function and design in molecular biology. Addison-Wesley, Reading.
[31]	McAdams, H.H. and Arkin, A. (1999) It’s a noisy busi ness! Genetic regulation at the nanomolar scale. Trends in Genetics, 15, 65-69. doi:10.1016/S0168-9525(98)01659-X
[32]	Ross, I.L., Browne, C.M. and Hume, D.A. (1994) Tran scription of individual genes in eukaryotic cells occurs randomly and infrequently. Immunology & Cell Biology, 72, 177-185. doi:10.1038/icb.1994.26
[33]	Bae, K., Lee, C., Hardin, P.E. and Edery, I. (2000) dCLOCK is present in limiting amounts and likely mediates daily interactions between the dCLOCK-CYC tran scription factor and the PER-TIM complex. Journal of Neuroscience, 20, 1746-1753.
[34]	Guptasarma, P. (1995) Does replication-induced tran scription regulate synthesis of the myriad low copy num ber proteins of Escherichia coli? Bioessays, 17, 987-997. doi:10.1002/bies.950171112
[35]	Bailone, A., Levine, A. and Devoret, R. (1979) Inactivation of prophage λ repressor in vivo. Journal of Molecular Biology, 131, 553-572. doi:10.1016/0022-2836(79)90007-X
[36]	Shea, M.A. and Ackers, G.K. (1985) The OR control system of bacteriophage λ. A physical-chemical model for gene regulation. Journal of Molecular Biology, 181, 211-230. doi:10.1016/0022-2836(85)90086-5
[37]	J. Paulsson. (2005) Models of stochastic gene expression. Physics of Life Reviews, 2, 157-175. doi:10.1016/j.plrev.2005.03.003
[38]	Ioannis, A.M., Andrei, D. and Dimitris, T. (2010) Gene regulatory networks modelling using a dynamic evolutionary hybrid. BMC Bioinformatics, 11, 140. doi:10.1186/1471-2105-11-140
[39]	Du, P., Gong, J., Wurtele, E.S. and Dickerson, J.A. (2005) Modeling gene expression networks using fuzzy logic. IEEE Transacions on Systems, Man and Cybernetics, 35, 1351-1359. doi:10.1109/TSMCB.2005.855590