RFMapp: Ribosome Flow Model Application

The Blavatnik School of Computer Science, Faculty of Exact Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.
Bioinformatics (Impact Factor: 4.98). 04/2012; 28(12):1663-4. DOI: 10.1093/bioinformatics/bts185
Source: PubMed

ABSTRACT The RFMapp is a graphical user interface application based on the RFM (ribosome flow model), enabling the estimation of the translation elongation rates of messenger ribonucleic acids (mRNAs) and the profile of ribosomal densities along the mRNAs, in a computationally efficient way. The RFMapp is based on the approach previously described by Reuveni et al., and unlike other traditional approaches in the field, which are mainly related to the genes' mean codon translation efficiency, the RFM additionally considers the codon order, the ribosomes' size and their order. Thus, it has been shown that RFM outperforms traditional predictors when analyzing both heterologous and endogenous genes. AVAILABILITY AND IMPLEMENTATION: Distributable cross-platform application and guideline are available for download at:

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Available from: Tamir Tuller, Jan 09, 2014
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    • "Summarizing, the RFM is a deterministic model for translation-elongation, and perhaps also other stages of gene expression [56], [14], that is highly amenable to analysis. "
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    ABSTRACT: Gene translation is the process in which intracellular macro-molecules, called ribosomes, decode genetic information in the mRNA chain into the corresponding proteins. Gene translation includes several steps. During the elongation step, ribosomes move along the mRNA in a sequential manner and link amino-acids together in the corresponding order to produce the proteins. The homogeneous ribosome flow model(HRFM) is a deterministic computational model for translation-elongation under the assumption of constant elongation rates along the mRNA chain. The HRFM is described by a set of n first-order nonlinear ordinary differential equations, where n represents the number of sites along the mRNA chain. The HRFM also includes two positive parameters: ribosomal initiation rate and the (constant) elongation rate. In this paper, we show that the steady-state translation rate in the HRFM is a concave function of its parameters. This means that the problem of determining the parameter values that maximize the translation rate is relatively simple. Our results may contribute to a better understanding of the mechanisms and evolution of translation-elongation. We demonstrate this by using the theoretical results to estimate the initiation rate in M. musculus embryonic stem cell. The underlying assumption is that evolution optimized the translation mechanism. For the infinite-dimensional HRFM, we derive a closed-form solution to the problem of determining the initiation and transition rates that maximize the protein translation rate. We show that these expressions provide good approximations for the optimal values in the n-dimensional HRFM already for relatively small values of n. These results may have applications for synthetic biology where an important problem is to re-engineer genomic systems in order to maximize the protein production rate.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics 07/2014; 11(6). DOI:10.1109/TCBB.2014.2330621 · 1.44 Impact Factor
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    ABSTRACT: Gene expression is a fundamental cellular process by which proteins are synthesized based on the information coded in the genes. Understanding, modeling and engineering this process have both important biotechnological applications and contributions to basic life science. In this paper I describe the flow models - a new approach for computationally modeling the biophysics of gene expression. The flow models can describe various stages of gene expression in a computationally efficient manner; in addition, they can be more readily analyzed mathematically than alternative models. Thus, these models provide a comprehensive framework for in-silico engineering of gene expression.
    Electrical & Electronics Engineers in Israel (IEEEI), 2012 IEEE 27th Convention of; 01/2012
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    ABSTRACT: Dozens of papers have been written about the relationship between codon bias, transcript features and gene translation. Even though answering these questions may sound straightforward, apparently many of these studies seem to contradict each other. In the present article, I provide four major non-mutually exclusive explanations related to this issue: (i) there are dozens of related relevant variables with unknown causal relationships; (ii) various biases in the relevant experimental data; (iii) drawing conclusions from specific examples; and (iv) challenges in experimentally modifying one biological variable without affecting the system via multiple biological feedback mechanisms. Specifically, some of the contradictions can be settled when considering these four points and/or via a multidisciplinary approach. The discussion reported in the present article is also relevant to many other biological/medical questions/fields.
    Biochemical Society Transactions 02/2014; 42(1):155-9. DOI:10.1042/BST20130095 · 3.19 Impact Factor
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