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Information theory, gene expression, and combinatorial regulation: a quantitative analysis

Overview of attention for article published in Theory in Biosciences, May 2013
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Title
Information theory, gene expression, and combinatorial regulation: a quantitative analysis
Published in
Theory in Biosciences, May 2013
DOI 10.1007/s12064-013-0182-7
Pubmed ID
Authors

Jürgen Jost, Klaus Scherrer

Abstract

According to a functional definition of the term "gene", a protein-coding gene corresponds to a polypeptide and, hence, a coding sequence. It is therefore as such not yet present at the DNA level, but assembled from possibly heterogeneous pieces in the course of RNA processing. Assembly and regulation of genes require, thus, information about when and in which quantity specific polypeptides are to be produced. To assess this, we draw upon precise biochemical data. On the basis of our conceptual framework, we also develop formal models for the coordinated expression of specific sets of genes through the interaction of transcripts and mRNAs and with proteins via a precise putative regulatory code. Thus, the nucleotides in transcripts and mRNA are not only arranged into amino acid-coding triplets, but at the same time may participate in regulatory oligomotifs that provide binding sites for specific proteins. We can then quantify and compare product and regulatory information involved in gene expression and regulation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 33%
Student > Bachelor 5 24%
Student > Ph. D. Student 3 14%
Student > Master 2 10%
Other 1 5%
Other 1 5%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 33%
Biochemistry, Genetics and Molecular Biology 5 24%
Computer Science 2 10%
Mathematics 1 5%
Arts and Humanities 1 5%
Other 3 14%
Unknown 2 10%