DOI: 10.5176/978-08-8227-3_BICB2011-33

Authors: Jose C. Nacher and Vladimir B. Ryabov


Recent data analyses have highlighted the existence of simple laws that govern the transcriptional organization. In spite of its importance, theoretical frameworks have not always kept pace with these findings and do not capture them simultaneously. Here we propose a simple probabilistic model that is able to reproduce two keystone statistical properties of the gene expression profiles: (1) Power-law decay of the expression level distribution function. (2) Approximately linear dependence between the temporal variation in the expression level and its initial value (also known as rich-travels-more). The developed statistical framework is based on the theory of random signals and noise (RSN) and it leads to new insights about the underlying dynamics of the coupling between gene expression and external signals. The model not only makes predictions on the gene expression response to the action of different pathogen-associated molecular patterns but also leads to identification of genes that are most strongly induced by

Keywords: gene expression profiles; transcriptional and signaling pathways; theory of random signals and noise; data analysis

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