Evidence map›Paper›PMID 27591251›Full record

ArticleNucleic acids research2016

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution.

Hadas Zur, Tamir Tuller

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

  1. Article
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  4. Negative feedback and oscillations in a model for mRNA translation.Journal of the Royal Society, Interface · 2025
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  11. Review: Emerging roles of the signaling network of the protein kinase GCN2 in the plant stress response.Plant science : an international journal of experimental plant biology · 2022
    Review
  12. Article
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  17. The effects of codon bias and optimality on mRNA and protein regulation.Cellular and molecular life sciences : CMLS · 2021
    Review
  18. Article
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  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hadas ZurDepartment of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv 69978, Israel.
Tamir TullerDepartment of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv 69978, Israel tamirtul@post.tau.ac.il.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

Indexed as

Models, BiologicalProtein BiosynthesisAlgorithmsAnticodonBiological EvolutionCodonModels, MolecularMolecular ConformationRNA, MessengerRNA, TransferStructure-Activity RelationshipAnticodonCodonRNA, MessengerRNA, Transfer

Identifiers

PMID27591251
PMCPMC5100582

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.