Abstract
We present an approach to inferring probabilistic models of gene-regulatory networks that is intended to provide a more mechanistic representation of transcriptional regulation than previous methods. Our approach involves learning Bayesian network models using both gene-expression and genomic-sequence data. One key aspect of our approach is that our models represent states of regulators in addition to their expression levels. For example, the state of a transcription factor may be determined by whether a particular small molecule is bound to it or not. Our models represent these states using hidden nodes in the Bayesian networks. A second key aspect of our approach is that we use known and predicted transcription start sites to determine whether a given transcription factor is more likely to act as an activator or a repressor for a given gene. We refer to this distinction as the role of a regulator with respect to a gene. Determining the roles of a regulator provides a helpful bias in learning accurate representations of regulator states. We evaluate our approach using sequence and expression data for E. coli K-12. Our experiments show that our models are comparable to, or better than, several baselines in terms of predictive accuracy. Moreover, they have more explanatory power than either baseline.
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Noto, K., Craven, M. (2005). Learning Regulatory Network Models that Represent Regulator States and Roles. In: Eskin, E., Workman, C. (eds) Regulatory Genomics. RRG 2004. Lecture Notes in Computer Science(), vol 3318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32280-1_6
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DOI: https://doi.org/10.1007/978-3-540-32280-1_6
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