Inferring rules of Escherichia coli translational efficiency using an artificial neural network.

Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Research Abstract Details 

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Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Abstract Text:

Koya Mori,Rintaro Saito,Shinichi Kikuchi,Masaru Tomita,

Although the machinery for translation initiation in Escherichia coli is very complicated, the translational efficiency has been reported to be predictable from upstream oligonucleotide sequences. Conventional models have difficulties in their generalization ability and prediction nonlinearity and in their ability to deal with a variety of input attributions. To address these issues, we employed structural learning by artificial neural networks to infer general rules for translational efficiency. The correlation between translational activities measured by biological experiments and those predicted by our method in the test data was significant (r=0.78), and our method uncovered underlying rules of translational activities and sequence patterns from the obtained skeleton structure. The significant rules for predicting translational efficiency were (1) G- and A-rich oligonucleotide sequences, resembling the Shine-Dalgarno sequence, at positions -10 to -7; (2) first base A in the initiation codon; (3) transport/binding or amino acid metabolism gene function; (4) high binding energy between mRNA and 16S rRNA at positions -15 to -5. An additional inferred novel rule was that C at position -1 increases translational efficiency. When our model was applied to the entire genomic sequence of E. coli, translational activities of genes for metabolism and translational were significantly high.

Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Publishing Authors By Initials

K Mori,R Saito,S Kikuchi,M Tomita,

For similar biological sciences: biology: computational biology: systems biology research abstracts see: biological sciences: biology: computational biology: systems biology research

PUBMED ID PMID:

MEDLINE DATE: 2007 Sep-Oct

Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Bio Systems

VOLUME: 90

Page Numbers: 414-20

Journal Abbreviation: BioSystems

ISSN: 0303-2647

DAY: 26

MONTH: 10

YEAR: 2006

Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Information

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LANGUAGE: eng

NlmUniqueID: 430773

Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Keywords Mesh Terms:

KEYWORDS: Systems Biology

MESH TERMS: metabolism

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Grant and Affiliation Information for Inferring rules of Escherichia coli translational efficiency using an artificial neural network.

AFFILIATION: Institute for Advanced Biosciences, Keio University, Baba 14-1, Tsuruoka, Yamagata 997-0035, Japan.

Country: Ireland

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MEDLINETA: Biosystems

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