TY - GEN
T1 - A Ribosome Flow Model for Analyzing Translation Elongation
T2 - 15th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2011
AU - Reuveni, Shlomi
AU - Meilijson, Isaac
AU - Kupiec, Martin
AU - Ruppin, Eytan
AU - Tuller, Tamir
N1 - Publisher Copyright:
© 2011, Springer-Verlag Berlin Heidelberg.
PY - 2011
Y1 - 2011
N2 - We describe the first genome wide analysis of translation based on a model aimed at capturing the physical and dynamical aspects of this process. The Ribosomal Flow Model (RFM) is a computationally efficient approximation of the Totally Asymmetric Exclusion Process (TASEP) model (e.g. see [1]). The RFM is sensitive to the order of codons in the coding sequence, the tRNA pool of the organism, interactions between ribosomes and their size (see Figure [1]). The RFM predicts fundamental outcomes of the translation process, including translation rates, protein abundance and ribosomal densities [2] and the relation between all these variables, better than alternative (’non-physical’) approaches (e.g. see [3,4]). In addition, we show that the RFM model can be used for accurate inference of initiation rates, the effect of codon order on protein abundance and the cost of translation. All these variables could not be inferred by previous predictors.
AB - We describe the first genome wide analysis of translation based on a model aimed at capturing the physical and dynamical aspects of this process. The Ribosomal Flow Model (RFM) is a computationally efficient approximation of the Totally Asymmetric Exclusion Process (TASEP) model (e.g. see [1]). The RFM is sensitive to the order of codons in the coding sequence, the tRNA pool of the organism, interactions between ribosomes and their size (see Figure [1]). The RFM predicts fundamental outcomes of the translation process, including translation rates, protein abundance and ribosomal densities [2] and the relation between all these variables, better than alternative (’non-physical’) approaches (e.g. see [3,4]). In addition, we show that the RFM model can be used for accurate inference of initiation rates, the effect of codon order on protein abundance and the cost of translation. All these variables could not be inferred by previous predictors.
UR - http://www.scopus.com/inward/record.url?scp=79953212569&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-20036-6_34
DO - 10.1007/978-3-642-20036-6_34
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AN - SCOPUS:79953212569
SN - 9783642200359
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 358
EP - 360
BT - Research in Computational Molecular Biology - 15th Annual International Conference, RECOMB 2011, Proceedings
A2 - Bafna, Vineet
A2 - Sahinalp, S. Cenk
PB - Springer Verlag
Y2 - 28 March 2011 through 31 March 2011
ER -