Evidence map›Paper›PMID 38048080›Full record

ReviewBriefings in bioinformatics2023

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.

Elena Lucy Carter, Chrystala Constantinidou, Mohammad Tauqeer Alam

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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  18. Preliminary Bioactivity Assessment ofJournal of fungi (Basel, Switzerland) · 2024
    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

3 authors.

Elena Lucy CarterWarwick Medical School, University of Warwick, Coventry, CV4 7HL, UK.
Chrystala ConstantinidouWarwick Medical School, University of Warwick.
Mohammad Tauqeer AlamUnited Arab Emirates University, Al Ain, UAE.

Funding

MRC Doctoral Training Partnership at the University of Warwick MR/N014294/1UAE University internal research G00003688UPAR G00004152
6 · The paper itself

Abstract

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Indexed as

AlgorithmsComputer Simulationemerging human pathogensenvironmental variationgenome-scale metabolic modelshost switchingmetabolic adaptation‘-omics’ datasimulationtools

Identifiers

PMID38048080
PMCPMC10694557

What OpenQuestion holds

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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.