Evidence map›Paper›PMID 30657938›Full record

ReviewMolecular biology and evolution2019

Adaptive Landscapes in the Age of Synthetic Biology.

Xiao Yi, Antony M Dean

Abstract readReview
In one paragraph

Review in Molecular biology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Weakest link epistasis and the geometry of genetic load.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
  7. Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. DeconstructingiScience · 2023
    Article
  15. Global epistasis on fitness landscapes.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023
    Review
  16. Article
  17. Exploring the expanse between theoretical questions and experimental approaches in the modern study of evolvability.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2023
    Article
  18. Review
  19. Article
  20. 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

2 authors.

Xiao YiBioTechnology Institute, University of Minnesota, St. Paul, MN.
Antony M DeanBioTechnology Institute, University of Minnesota, St. Paul, MN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For nearly a century adaptive landscapes have provided overviews of the evolutionary process and yet they remain metaphors. We redefine adaptive landscapes in terms of biological processes rather than descriptive phenomenology. We focus on the underlying mechanisms that generate emergent properties such as epistasis, dominance, trade-offs and adaptive peaks. We illustrate the utility of landscapes in predicting the course of adaptation and the distribution of fitness effects. We abandon aged arguments concerning landscape ruggedness in favor of empirically determining landscape architecture. In so doing, we transform the landscape metaphor into a scientific framework within which causal hypotheses can be tested.

Indexed as

Adaptation, BiologicalBiological EvolutionModels, Genetic3-Isopropylmalate DehydrogenaseAnimalsChemotaxisEscherichia coliGenetic FitnessGenotypeLac OperonLac RepressorsMethanolOpsinsSynthetic Biology3-Isopropylmalate DehydrogenaseLac RepressorsMethanolOpsinsadaptive landscapedistribution of fitness effectsepistasisgenotype by environment interactiongenotype–phenotype gappleiotropy

Identifiers

PMID30657938
PMCPMC6501885

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.