Evidence map›Paper›PMID 39482321›Full record

ArticleNature communications2024

Minimisation of metabolic networks defines a new functional class of genes.

Giorgio Jansen, Tanda Qi, Vito Latora, Grigoris D Amoutzias, Daniela Delneri, Stephen G Oliver, Giuseppe Nicosia

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Giorgio JansenDepartment of Biochemistry, University of Cambridge, Cambridge, UK.ORCID 0000-0001-7320-4053
Tanda QiManchester Institute of Biotechnology, University of Manchester, Manchester, UK.ORCID 0000-0002-2097-246X
Vito LatoraSchool of Mathematical Sciences, Queen Mary University of London, London, UK.ORCID 0000-0002-0984-8038
Grigoris D AmoutziasBioinformatics Laboratory, Department of Biochemistry & Biotechnology, University of Thessaly, Thessaly, Greece.ORCID 0000-0001-5961-964X
Daniela DelneriManchester Institute of Biotechnology, University of Manchester, Manchester, UK.ORCID 0000-0001-8070-411X
Stephen G Oliver *Department of Biochemistry, University of Cambridge, Cambridge, UK. sgo24@cam.ac.uk.ORCID 0000-0003-3410-6439
Giuseppe Nicosia *Department of Biochemistry, University of Cambridge, Cambridge, UK. giuseppe.nicosia@unict.it.ORCID 0000-0002-0650-3157

Funding

RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/N02348X/1
6 · The paper itself

Abstract

Construction of minimal metabolic networks (MMNs) contributes both to our understanding of the origins of metabolism and to the efficiency of biotechnological processes by preventing the diversion of flux away from product formation. We have designed MMNs using a novel in silico synthetic biology pipeline that removes genes encoding enzymes and transporters from genome-scale metabolic models. The resulting minimal gene-set still ensures both viability and high growth rates. The composition of these MMNs has defined a new functional class of genes termed Network Efficiency Determinants (NEDs). These genes, whilst not essential, are very rarely eliminated in constructing an MMN, suggesting that it is difficult for metabolism to be re-routed to obviate the need for such genes. Moreover, the removal of NED genes from an MMN significantly reduces its global efficiency. Bioinformatic analyses of the NED genes have revealed that not only do these genes have more genetic interactions than the bulk of metabolic genes but their protein products also show more protein-protein interactions. In yeast, NED genes are predominantly single-copy and are highly conserved across evolutionarily distant organisms. These features confirm the importance of the NED genes to the metabolic network, including why they are so rarely excluded during minimisation.

Indexed as

Metabolic Networks and PathwaysSaccharomyces cerevisiaeComputational BiologyComputer SimulationSynthetic Biology

Identifiers

PMID39482321
PMCPMC11528065

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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.