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Discovering genetic regularory networks.

M.J.T. Reinders, E.P. van Someren, R.J.P. van Berlo and L.F.A. Wessels

Recent advances in genomics research and cDNA and oligonucleotide microarray technologies have made it possible to measure the mRNA expression levels on a genome-wide scale1,2. Measuring the transcriptional activity of genes within a cell over time in different environmental conditions provides information about the regulatory mechanisms between genes. Inferring the gene regulatory network from transcriptional data is known as genetic network modeling3,4. Since genomes typically consists of thousands of genes and the experimental data is expensive to obtain, the reverse engineering of the network topology is an ill-posed problem. To alleviate the problem of data scarcity, many research efforts focus on clustering, i.e. grouping genes into functional units based on correlations in expression patterns5,6,7,8. Although clustering is a useful tool to identify co-regulation, it does not take into account the dynamics that take place within a cell.

Recently, there have been attempts to follow a system identification approach to build dynamic models for gene regulatory networks on a genome-wide scale. For example, models based on differential equations (solved with genetic algorithms)9, neural networks10,11, Bayesian models12,13,14, and linear models15,16,17. Comparative studies18 have shown that these models perform poorly when faced with limited data and noise on the measurements. Therefore, the modeling process should be constrained by employing biologically motivated knowledge about genetic regulation, such as sparseness, redundancy, stability and robustness. We will discuss how a number of such general constraints can be incorporated in the modeling process19,20,21. Further, we will show preliminary results of the application of these techniques to discover the regulatory mechanisms behind osteoblast differentiation.

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  2. Lockhart, D.J., and Winzeler, E.A. Genomics, gene expression and DNA arrays, Nature, 405:827-836, 2000.

  3. De Jong, H. Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology. 9(1):67-103, 2002.

  4. van Someren, E. P., Wessels, L. F.A., Backer, E., and Reinders, M. J. T. Genetic network modeling. Pharmacogenomics, 3 (4):507-525, 2002.

  5. Arkin, A., Shen, P., and Ross, J. A test case of correlation matrix construction of a reaction pathway from measurements, Science, 277:1275-1279, 1997

  6. Segal, E., Shapira, M., Regev, A. Pe'er, D., Botstein, D., Koller, D., and Friedman, N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature genetics, 34(2):166,176, 2003.

  7. Tavazoi, S. Hughes, J.D., Campbell, M.J., Cho, R.J., and Church, G.M. Systematic determination of genetic network architecture. Nature genetics, 22:281-285, 1999.

  8. Wen, X. et al. Large-scale temporal gene expression mapping of central nervous system development, Proc. Natl. Acad. Sci. USA, 95:334-339, 1998.

  9. Wahde, M., Hertz, J. Modeling genetic regulatory dynamics in neural development. Journal of computational biology, 8(4):429-442, 2001.

  10. D'haeseleer, P., Liang, S., and Somogyi, R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 16(8):707-726, 2000.

  11. Weaver, D.C., Workman, C.T., and Stormo, G.D. Modeling regulatory networks with weight matrices, Pacific Symposium on Biocomputing, 112-123, 1999.

  12. Berlo, R.J.P., van Someren, E.P., and Reinders, M.J.T. Studying the conditions for learning dynamic Bayesian networks to discover genetic regulatory networks, Simulation, 79(12):689-702, 2003

  13. Hartemink, A., Gifford, D.K., Jaakola, T.S., and Young, R.A. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks, Pacific Symposium on Biocomputing, 422-433, 2001

  14. Pe'er, D., Regev, A., Elidan, G., and Friedman, N. Inferring subnetworks from perturbed expression profiles. Bioinformatics, 1(1):1-9, 2001.

  15. D'Haeseleer, P., Wen, X., Fuhrman, S., and Somogyi, R. Linear modeling of mRNA expression levels curing CNS development and injury. Pacific symposium on biocomputing, 4:41-52, 1999.

  16. van Someren, E.P., Wessels, L.F.A., and Reinders, M.J.T. Linear Modeling of Genetic Networks from Experimental Data, In proceedings of the 8th international conference on intelligent systems for molecular biology, 355-366, 2000.

  17. Yeung, M.K., Tegner, J., and Collins, J.J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA, 99(9):6163-6168, 2002.

  18. Wessels, L.F.A., van Someren, E.P., and Reinders, M.J.T. A comparison of genetic network models. Pacific symposium on biocomputing, 6:508-519, 2001.

  19. van Someren, E.P., Wessels, L.F.A., Reinders, M.J.T., and Backer, E. Searching for limited connectivity in genetic network models . In proceedings of the 2nd international conference on systems biology, 222-230, 2001.

  20. van Someren, E.P., Wessels, L.F.A., Reinders, M.J.T., and Backer, E. Regularization and noise injection for improving genetic network models. Computational and Statistical Approaches to Genomics. Chapter 12, Kluwer, 211-226, 2002.

  21. van Someren, E.P., Wessels, L.F.A., Backer, E., and Reinders, M.J.T. Multi-criterion optimization for genetic network modeling, Signal Processing, 83:763-775, 2003

Last modified: 9 June 2004, 14:49 UTC
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