A Comprehensive, Quantitative, and Genome-Wide Model of Translation
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{"title"=>"A comprehensive, quantitative, and genome-wide model of translation", "type"=>"journal", "authors"=>[{"first_name"=>"Marlena", "last_name"=>"Siwiak", "scopus_author_id"=>"36572431000"}, {"first_name"=>"Piotr", "last_name"=>"Zielenkiewicz", "scopus_author_id"=>"7004305289"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"78049245511", "scopus"=>"2-s2.0-78049245511", "doi"=>"10.1371/journal.pcbi.1000865", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pui"=>"359856053", "issn"=>"1553734X", "pmid"=>"20686685"}, "id"=>"e50e92c4-277b-3a8e-a363-83dc89a2d8f0", "abstract"=>"<title>Author Summary</title> <p>Translation is the production of proteins by decoding mRNA produced in transcription, and is a part of the overall process of gene expression. Although the general theoretical background of translation is known, the process is still poorly characterised at the level of individual proteins. In particular, the quantitative parameters of translation, such as time required to complete it or the number of protein molecules produced from a transcript during its lifetime, are extremely difficult to measure experimentally. To overcome this problem, we developed a computational model that, on the basis of only few datasets and general assumptions, measures quantitatively the translational activity at the level of individual genes. We discussed it concerning the example of the yeast system; however, it can be applied to any organism of known genome. We used the obtained results to study the general characteristics of the yeast translational system, revealing the diversity of strategies of gene expression regulation. We exemplified and discussed other possible ways of model utilisation, as it may help in examining protein-protein interactions, metabolic pathways, gene annotation, ribosome queueing, protein folding, and translation initiation. It also may be crucial for better integration of cell-wide, high-throughput experiments.</p>", "link"=>"http://www.mendeley.com/research/comprehensive-quantitative-genomewide-model-translation", "reader_count"=>168, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>14, "Student > Doctoral Student"=>5, "Researcher"=>51, "Student > Ph. D. Student"=>52, "Student > Postgraduate"=>5, "Other"=>6, "Student > Master"=>16, "Student > Bachelor"=>3, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>14, "Student > Doctoral Student"=>5, "Researcher"=>51, "Student > Ph. D. Student"=>52, "Student > Postgraduate"=>5, "Other"=>6, "Student > Master"=>16, "Student > Bachelor"=>3, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Engineering"=>6, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>20, "Mathematics"=>1, "Agricultural and Biological Sciences"=>113, "Medicine and Dentistry"=>2, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>5, "Computer Science"=>8, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>6}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>113}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>20}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>10}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>14, "Japan"=>1, "United Kingdom"=>5, "Portugal"=>2, "Spain"=>2, "India"=>1, "Canada"=>1, "Netherlands"=>1, "Poland"=>2, "Italy"=>1, "France"=>2, "Peru"=>1, "Germany"=>2}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/837653"], "description"=>"<p>The bottom plot shows all of the translation initiation events during the mean lifetime of one mRNA molecule. Translation initiations are marked with ribosome-shaped symbols. The orange line indicates the mean lifetime of YJL173C mRNA. The broken curves' slope depicts the rate of polypeptide chain growth measured at particular codons. The number of curves indicates the number of protein molecules (here 46) produced from one mRNA during its lifetime. The top-right plot shows, in magnitude, the translation of the first protein molecule (darkbrown curve). The time is measured since the transcript becomes accessible to the translation machinery. The first seconds are spent on translation initiation; elongation begins after about 10 sec. Red dots mark ribosome positions in time (dotted blue lines) and space (dashed blue lines) when the following ribosomes attach to the mRNA molecule. The histogram on the left shows the mean translation times of particular codons of the YJL173C sequence. The dashed black line is the mean time of translation of one codon of the YJL173C mRNA sequence.</p>", "links"=>[], "tags"=>["biochemistry/transcription and translation", "cell biology/gene expression", "computational biology/systems biology", "molecular biology/bioinformatics", "molecular biology/translation mechanisms", "molecular biology/translational regulation"], "article_id"=>508018, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Translation_model_of_YJL173C_/508018", "title"=>"Translation model of YJL173C.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-29 02:13:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/837762"], "description"=>"<p>The plots show the comparison of model parameters (left) and (right) with experimentally determined mRNA and protein abundances by two independent studies <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Gygi1\" target=\"_blank\">[13]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Futcher1\" target=\"_blank\">[26]</a>. The axes were log transformed. Calculated values are presented in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi-1000865-t002\" target=\"_blank\">Table 2</a>. The distribution of the log-fold differences of the mRNA and protein concentrations reported by the model and reference studies are presented in Supplementary <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865.s001\" target=\"_blank\">Figure S1</a>.</p>", "links"=>[], "tags"=>["biochemistry/transcription and translation", "cell biology/gene expression", "computational biology/systems biology", "molecular biology/bioinformatics", "molecular biology/translation mechanisms", "molecular biology/translational regulation"], "article_id"=>508135, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_results_vs_experimental_studies_/508135", "title"=>"Model results vs experimental studies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-29 02:15:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/838091"], "description"=>"<p>Column descriptions: (1) name of the parameter; (2) mean value; (3) median value; (4) standard deviation; (5) minimal observed value; (6) maximal observed value; and (7) parameter description. For all parameters, except , , and , the columns 2, 3, 4, 5, and 6 were calculated over the entire dataset of 4,470 yeast genes. For parameters , , and the columns 2, 3, 4, 5, and 6 were calculated over the set of 4,192 genes.</p>", "links"=>[], "tags"=>["translational", "parameters", "calculated"], "article_id"=>508466, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_translational_parameters_calculated_in_the_model_/508466", "title"=>"The translational parameters calculated in the model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-29 02:21:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/837993"], "description"=>"<p>The plot shows the coparison of translation times in 30C of individual yeast codons with codon optimality values calculated by <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Zhou1\" target=\"_blank\">[36]</a>. There is negative correlation between value and translation time of a codon. However, while optimal codons (high values) have only short times of translation, non-optimal codons may be translated at both high and low rates. Adjusted value obtained in linear regression model through the origin on log transformed values indicates, that translation speed may explain only 15% of variability in values.</p>", "links"=>[], "tags"=>["optimality"], "article_id"=>508366, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.g005", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Codon_optimality_vs_translation_time_/508366", "title"=>"Codon optimality vs translation time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-29 02:19:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/838059"], "description"=>"<p>The distribution of translational parameter values for the set of 20 genes having high protein production rates () and relatively low transcriptional activity (). Column descriptions: (1) name of the parameter; (2) median value; (3) minimal observed value; and (4) maximal observed value. The units are the same as those presented in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi-1000865-t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["parameters", "20", "genes", "transcriptional"], "article_id"=>508434, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Translational_parameters_of_20_genes_of_low_transcriptional_activity_and_high_protein_production_rate_/508434", "title"=>"Translational parameters of 20 genes of low transcriptional activity and high protein production rate.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-29 02:20:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/838123"], "description"=>"<p>Values of , , and coefficients at four different temperatures. is the average time to insert an amino acid from a cognate aa-tRNA, and are the average time delays caused by the binding attempts by near- and non-cognate tRNA, respectively. All times are in ms.</p>", "links"=>[], "tags"=>["trnas", "insertions"], "article_id"=>508498, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.t004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_of_tRNAs_insertions_at_four_different_temperatures_/508498", "title"=>"Time of tRNAs insertions at four different temperatures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-29 02:21:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/417501", "https://ndownloader.figshare.com/files/417519", "https://ndownloader.figshare.com/files/417554", "https://ndownloader.figshare.com/files/417564", "https://ndownloader.figshare.com/files/417592", "https://ndownloader.figshare.com/files/417613"], "description"=>"<div><p>Translation is still poorly characterised at the level of individual proteins and its role in regulation of gene expression has been constantly underestimated. To better understand the process of protein synthesis we developed a comprehensive and quantitative model of translation, characterising protein synthesis separately for individual genes. The main advantage of the model is that basing it on only a few datasets and general assumptions allows the calculation of many important translational parameters, which are extremely difficult to measure experimentally. In the model, each gene is attributed with a set of translational parameters, namely the absolute number of transcripts, ribosome density, mean codon translation time, total transcript translation time, total time required for translation initiation and elongation, translation initiation rate, mean mRNA lifetime, and absolute number of proteins produced by gene transcripts. Most parameters were calculated based on only one experimental dataset of genome-wide ribosome profiling. The model was implemented in <em>Saccharomyces cerevisiae</em>, and its results were compared with available data, yielding reasonably good correlations. The calculated coefficients were used to perform a global analysis of translation in yeast, revealing some interesting aspects of the process. We have shown that two commonly used measures of translation efficiency – ribosome density and number of protein molecules produced – are affected by two distinct factors. High values of both measures are caused, i.a., by very short times of translation initiation, however, the origins of initiation time reduction are completely different in both cases. The model is universal and can be applied to any organism, if the necessary input data are available. The model allows us to better integrate transcriptomic and proteomic data. A few other possibilities of the model utilisation are discussed concerning the example of the yeast system.</p></div>", "links"=>[], "tags"=>["genome-wide"], "article_id"=>142414, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000865.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000865.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000865.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000865.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000865.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000865.s006"], "stats"=>{"downloads"=>36, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Comprehensive_Quantitative_and_Genome_Wide_Model_of_Translation/142414", "title"=>"A Comprehensive, Quantitative, and Genome-Wide Model of Translation", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-07-29 00:40:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/837936"], "description"=>"<p>The plot shows the correlation between mRNA abundance (parameter ) and the number of protein molecules produced from a given gene (parameter ). We performed linear regression through the origin on log transformed data. Adjusted value calculated over the entire dataset (4192 genes of known ) was 0.59. This means that over 40% (in log space) of the variation in protein abundance cannot be explained by variation in mRNA abundance, suggesting some additional, posttranscriptional mechanisms of gene expression regulation.</p>", "links"=>[], "tags"=>["mrna"], "article_id"=>508312, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_of_mRNA_and_protein_expression_levels_/508312", "title"=>"Correlation of mRNA and protein expression levels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-29 02:18:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/838034"], "description"=>"<p>The comparison of mRNA and protein abundances obtained in the model (reflected by parameters and ) with values reported by three independent experimental studies <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Gygi1\" target=\"_blank\">[13]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Futcher1\" target=\"_blank\">[26]</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Holstege1\" target=\"_blank\">[29]</a>. We performed a simple linear regression through the origin on the log-transformed values. Column descriptions: (common genes), number of common genes in two compared datasets; (adj. ), adjusted values for the linear regression model; and (), regression coefficient. The third row is the comparison of the two experimental studies with each other. All coefficients were statistically significant (F-statistic p-values ).</p>", "links"=>[], "tags"=>["mrna", "abundances"], "article_id"=>508409, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_determined_mRNA_and_protein_abundances_versus_experimental_studies_/508409", "title"=>"Model determined mRNA and protein abundances versus experimental studies.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-29 02:20:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/837876"], "description"=>"<p>Left plot: the comparison of model parameter with mRNA abundances determined by high-density oligonucleotide array (HDA) experiment <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi.1000865-Holstege1\" target=\"_blank\">[29]</a>. The axes were log transformed. Calculated value for the comparison is presented in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000865#pcbi-1000865-t002\" target=\"_blank\">Table 2</a>. Right plot: distribution of the log-fold differences of the mRNA concentrations reported by the model and reference study.</p>", "links"=>[], "tags"=>["transcript", "abundance"], "article_id"=>508252, "categories"=>["Molecular Biology", "Cell Biology", "Biochemistry", "Medicine"], "users"=>["Marlena Siwiak", "Piotr Zielenkiewicz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000865.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Calculated_transcript_abundance_vs_experimental_studies_/508252", "title"=>"Calculated transcript abundance vs experimental studies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-29 02:17:32"}

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Relative Metric

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