Evidence map›Paper›PMID 41082574›Full record

ArticlePLoS computational biology2025

A compact model of Escherichia coli core and biosynthetic metabolism.

Marco Corrao, Hai He, Wolfram Liebermeister, Elad Noor, Arren Bar-Even

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

5 authors.

Marco CorraoDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.ORCID 0009-0000-2113-5703
Hai HeDepartment of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.ORCID 0000-0003-1223-2813
Wolfram LiebermeisterUniversité Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France.
Elad NoorDepartment of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel.ORCID 0000-0001-8776-4799
Arren Bar-EvenGroup of Systems and Synthetic Metabolism, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.

Funding

UK Biotechnology and Biological Sciences Research Council BB/T008784/1
6 · The paper itself

Abstract

Metabolic models condense biochemical knowledge about organisms in a structured and standardised way. As large-scale network reconstructions are readily available for many organisms, genome-scale models are being widely used among modellers and engineers. However, these large models can be difficult to analyse and visualise, and occasionally generate predictions that are hard to interpret or even biologically unrealistic. Of the thousands of enzymatic reactions in a typical bacterial metabolism, only a few hundred form the metabolic pathways essential to produce energy carriers and biosynthetic precursors. These pathways carry relatively high flux, are central to maintaining and reproducing the cell, and provide precursors and energy to engineered metabolic pathways. Focusing on these central metabolic subsystems, we present iCH360, a manually curated medium-scale model of energy and biosynthesis metabolism for the well-studied bacterium Escherichia coli K-12 MG1655. The model is a sub-network of the most recent genome-scale reconstruction, iML1515, and comes with an updated layer of database annotations and a range of metabolic maps for visualisation. We enriched the stoichiometric network with extensive biological information and quantitative data, including thermodynamic and kinetic constants, enhancing the scope and applicability of the model. In addition, we assess the properties of this model in comparison to its genome-scale parent and demonstrate the use of the network and supporting data in various scenarios, including enzyme-constrained flux balance analysis, elementary flux mode analysis, and thermodynamic analysis. Overall, this model holds the potential to become a reference medium-scale metabolic model for E. coli.

Indexed as

Escherichia coliMetabolic Networks and PathwaysModels, BiologicalBiosynthetic PathwaysComputational Biology

Identifiers

PMID41082574
PMCPMC12533975

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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.