Evidence map›Paper›PMID 24031036›Full record

ReviewBiotechnology journal2013

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory.

Duygu Dikicioglu, Pınar Pir, Stephen G Oliver

Erratum issuedAbstract readReview
In one paragraph

Review in Biotechnology journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. The Model SystemLife (Basel, Switzerland) · 2020
    Review
  5. Article
  6. Chemogenetic interactions in human cancer cells.Computational and structural biotechnology journal · 2019
    Review
  7. Review
  8. Article
  9. The metabolome 18 years on: a concept comes of age.Metabolomics : Official journal of the Metabolomic Society · 2016
    Review
4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

3 authors.

Duygu DikiciogluCambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, CB2 1GA, Cambridge, UK.
Pınar Pir
Stephen G Oliver

Funding

Biotechnology and Biological Sciences Research Council BB/C505140/1
6 · The paper itself

Abstract

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Indexed as

Genetic EngineeringBiological EvolutionBiotechnologyComputational BiologyFermentationGene DosageGenes, EssentialGenes, FungalGenetic FitnessGenotypeHumansModels, BiologicalPhenotypeSaccharomyces cerevisiaeEngineered strainsFitnessGrowth controlHuman diseasesYeast

Identifiers

PMID24031036
PMCPMC3910164

What OpenQuestion holds

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LicenceCC BY
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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.