From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
Computational Dissection of Regulatory Networks Using Diverse High-Throughput Data
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
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The modeling of transcription regulation programs is a major focus of today's biology. The challenge is to utilize diverse high-throughput data (gene expression, promoter binding site localization assays, protein expression) in order to infer the mechanistic models of transcription control. We propose a new model which integrates transcription factor-gene affinities, protein abundance and gene expression levels. Transcription factor binding site activity is represented by a dose-affinity-response function, and regulation is assumed to be a combinatorial function of the activities of the binding sites in the gene's promoter sites.We develop algorithms that infer the model given complete data and give a fast polynomial time algorithm under reasonable assumptions. We also show how to assess initial values of missing data (notably protein abundance) using a novel framework for active motif detection, which may be of independent interest. We test the various components of the framework on gene expression data related to carbohydrate metabolism in yeast. The results demonstrate the high specificity and sensitivity of the approach and its advantages over extant motif activity detection methods. We are also able to predict new active motifs in the galactose pathway.A key feature of our method is the global approach to transcription factor activity and to the relation between this activity and promoter signals. We use dozens of genes, with many different promoter signals and expression levels in order to draw conclusions on the function of a single transcription factor. This provides us the robustness necessary in order to overcome the considerable level of noise in the data.