Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cerevisiae
1 Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, USA
2 Department of Biostatistics, University of California, Berkeley, CA, USA
3 Department of Radiology, University of Washington, WA, USA
4 Department of Applied Science, University of California, Davis, USA
BMC Bioinformatics 2006, 7:381 doi:10.1186/1471-2105-7-381Published: 16 August 2006
Reverse-engineering regulatory networks is one of the central challenges for computational biology. Many techniques have been developed to accomplish this by utilizing transcription factor binding data in conjunction with expression data. Of these approaches, several have focused on the reconstruction of the cell cycle regulatory network of Saccharomyces cerevisiae. The emphasis of these studies has been to model the relationships between transcription factors and their target genes. In contrast, here we focus on reverse-engineering the network of relationships among transcription factors that regulate the cell cycle in S. cerevisiae.
We have developed a technique to reverse-engineer networks of the time-dependent activities of transcription factors that regulate the cell cycle in S. cerevisiae. The model utilizes linear regression to first estimate the activities of transcription factors from expression time series and genome-wide transcription factor binding data. We then use least squares to construct a model of the time evolution of the activities. We validate our approach in two ways: by demonstrating that it accurately models expression data and by demonstrating that our reconstructed model is similar to previously-published models of transcriptional regulation of the cell cycle.
Our regression-based approach allows us to build a general model of transcriptional regulation of the yeast cell cycle that includes additional factors and couplings not reported in previously-published models. Our model could serve as a starting point for targeted experiments that test the predicted interactions. In the future, we plan to apply our technique to reverse-engineer other systems where both genome-wide time series expression data and transcription factor binding data are available.