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mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.

Two novel approaches were recently suggested for genome-wide identification of protein aspects synthesized at a given time. Ribo-Seq is based on sequencing all the ribosome protected mRNA fragments in a cell, while PUNCH-P is based on mass-spectrometric analysis of only newly synthesized proteins. Here we describe the first Ribo-Seq/PUNCH-P comparison via the analysis of mammalian cells during the cell-cycle for detecting relevant differentially expressed genes between G1 and M phase. Our analyses suggest that the two approaches significantly overlap with each other. However, we demonstrate that there are biologically meaningful proteins/genes that can be detected to be post-transcriptionally regulated during the mammalian cell cycle only by each of the approaches, or their consolidation. Such gene sets are enriched with proteins known to be related to intra-cellular signalling pathways such as central cell cycle processes, central gene expression regulation processes, processes related to chromosome segregation, DNA damage, and replication, that are post-transcriptionally regulated during the mammalian cell cycle. Moreover, we show that combining the approaches better predicts steady state changes in protein abundance. The results reported here support the conjecture that for gaining a full post-transcriptional regulation picture one should integrate the two approaches.

Unlabelled: 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: http://www.cs.tau.ac.il/~tamirtul/RFM_Installers/install.htm.