Evidence map›Paper›PMID 27683361›Full record

ArticleProceedings. Biological sciences2016

Phenotypic innovation through recombination in genome-scale metabolic networks.

Sayed-Rzgar Hosseini, Olivier C Martin, Andreas Wagner

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Protein cost minimization promotes the emergence of coenzyme redundancy.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  3. Article
  4. Article
  5. Article
  6. 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.

Sayed-Rzgar HosseiniInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Building Y27, Winterthurerstrasse 190, 8057 Zurich, Switzerland The Swiss Institute of Bioinformatics, Quartier Sorge, Batiment Genopode, 1015 Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-2308-6754
Olivier C MartinGQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190 Gif-sur-Yvette, France.
Andreas WagnerInstitute of Evolutionary Biology and Environmental Studies, University of Zurich, Building Y27, Winterthurerstrasse 190, 8057 Zurich, Switzerland The Swiss Institute of Bioinformatics, Quartier Sorge, Batiment Genopode, 1015 Lausanne, Switzerland The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA andreas.wagner@ieu.uzh.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype-phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

Indexed as

genome-scale metabolic networksmetabolic genotypemetabolic innovationmetabolic phenotyperecombinationsuperessential reactions

Identifiers

PMID27683361
PMCPMC5046906

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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.