Evidence map›Paper›PMID 34590582›Full record

ArticleeLife2021

First-principles model of optimal translation factors stoichiometry.

Jean-Benoît Lalanne, Gene-Wei Li

Abstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Jean-Benoît LalanneDepartment of Biology, Massachusetts Institute of Technology, Cambridge, United States.ORCID 0000-0001-8753-0669
Gene-Wei LiDepartment of Biology, Massachusetts Institute of Technology, Cambridge, United States.ORCID 0000-0001-7036-8511

Funding

Evolution and Regulation of Bacterial Proteome CompositionR35GM124732 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Gene-Wei Li · 2017 to 2026
$3.8M
Howard Hughes Medical InstituteNIGMS NIH HHS R35 GM124732
6 · The paper itself

Abstract

Enzymatic pathways have evolved uniquely preferred protein expression stoichiometry in living cells, but our ability to predict the optimal abundances from basic properties remains underdeveloped. Here, we report a biophysical, first-principles model of growth optimization for core mRNA translation, a multi-enzyme system that involves proteins with a broadly conserved stoichiometry spanning two orders of magnitude. We show that predictions from maximization of ribosome usage in a parsimonious flux model constrained by proteome allocation agree with the conserved ratios of translation factors. The analytical solutions, without free parameters, provide an interpretable framework for the observed hierarchy of expression levels based on simple biophysical properties, such as diffusion constants and protein sizes. Our results provide an intuitive and quantitative understanding for the construction of a central process of life, as well as a path toward rational design of pathway-specific enzyme expression stoichiometry.

Indexed as

Protein BiosynthesisBacteriaEnzymesGene Expression Regulation, BacterialModels, TheoreticalProteomeRibosomesEnzymesProteomeB. subtiliscomputational biologyE. coliexpression stoichiometrymRNA translation factorsphysics of living systemspredictive modelsystems biology

Identifiers

PMID34590582
PMCPMC8530515

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.