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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