Evidence map›Paper›PMID 36699126›Full record

ReviewEvolutionary applications2023

Towards evolutionary predictions: Current promises and challenges.

Meike T Wortel, Deepa Agashe, Susan F Bailey, Claudia Bank, Karen Bisschop, Thomas Blankers, Johannes Cairns, Enrico Sandro Colizzi, Davide Cusseddu, Michael M Desai and 26 more

Abstract readReview
In one paragraph

Review in Evolutionary applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Review
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  14. Founders predict trait evolution and population performance after evolutionary rescue in the red flour beetle.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  15. Evolution and evolvability of rifampicin resistance across the bacterial tree of life.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  16. Implications From the Analogous Relationship Between Evolutionary and Learning Processes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025
    Review
  17. Article
  18. Article
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  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

36 authors.

Meike T WortelSwammerdam Institute for Life Sciences University of Amsterdam Amsterdam The Netherlands.ORCID https://orcid.org/0000-0002-3191-2710
Deepa AgasheNational Centre for Biological Sciences Bangalore India.ORCID https://orcid.org/0000-0002-0374-8159
Susan F BaileyClarkson University Potsdam New York USA.ORCID https://orcid.org/0000-0002-2294-1229
Claudia BankInstitute of Ecology and Evolution University of Bern Bern Switzerland.ORCID https://orcid.org/0000-0003-4730-758X
Karen BisschopInstitute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands.ORCID https://orcid.org/0000-0001-7083-2636
Thomas BlankersInstitute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands.ORCID https://orcid.org/0000-0002-1893-8537
Johannes CairnsUniversity of Helsinki Helsinki Finland.ORCID https://orcid.org/0000-0003-1329-2025
Enrico Sandro ColizziOrigins Center Groningen The Netherlands.ORCID https://orcid.org/0000-0003-1709-4499
Davide CussedduInstituto Gulbenkian de Ciência Oeiras Portugal.ORCID https://orcid.org/0000-0002-6882-9486
Michael M DesaiHarvard University Cambridge Massachusetts USA.
Bram van DijkMax Planck Institute for Evolutionary Biology Plön Germany.
Martijn EgasInstitute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands.ORCID https://orcid.org/0000-0001-8078-1609
Jacintha EllersDepartment of Ecological Science Vrije Universiteit Amsterdam Amsterdam The Netherlands.ORCID https://orcid.org/0000-0003-2665-1971
Astrid T GrootInstitute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands.ORCID https://orcid.org/0000-0001-9595-0161
David G HeckelMax Planck Institute for Chemical Ecology Jena Germany.ORCID https://orcid.org/0000-0001-8991-2150
Marcelle L JohnsonNetherlands Institute of Ecology (NIOO-KNAW) Wageningen The Netherlands.ORCID https://orcid.org/0000-0001-5599-4813
Ken KraaijeveldLeiden Centre for Applied Bioscience University of Applied Sciences Leiden Leiden The Netherlands.ORCID https://orcid.org/0000-0002-4676-0705
Joachim KrugInstitute for Biological Physics University of Cologne Cologne Germany.ORCID https://orcid.org/0000-0002-2143-6490
Liedewij LaanDepartment of Bionanoscience, Kavli Institute of Nanoscience TU Delft Delft The Netherlands.ORCID https://orcid.org/0000-0002-7138-9004
Michael LässigInstitute for Biological Physics University of Cologne Cologne Germany.ORCID https://orcid.org/0000-0002-3575-6719
Peter A LindDepartment Molecular Biology Umeå University Umeå Sweden.ORCID https://orcid.org/0000-0003-1510-8324
Jeroen MeijerTheoretical Biology and Bioinformatics, Department of Biology Utrecht University Utrecht The Netherlands.ORCID https://orcid.org/0000-0003-3258-2431
Luke M NobleInstitute de Biologie, École Normale Supérieure, CNRS, Inserm Paris France.
Samir OkashaUniversity of Bristol Bristol UK.
Paul B RaineyDepartment of Microbial Population Biology Max Planck Institute for Evolutionary Biology Plön Germany.ORCID https://orcid.org/0000-0003-0879-5795
Daniel E RozenInstitute of Biology, Leiden University Leiden The Netherlands.ORCID https://orcid.org/0000-0002-7772-0239
Shraddha ShitutOrigins Center Groningen The Netherlands.ORCID https://orcid.org/0000-0002-2759-6508
Sander J TansAMOLF Amsterdam The Netherlands.
Olivier TenaillonUniversité Paris Cité, IAME, UMR 1137, INSERM Paris France.ORCID https://orcid.org/0000-0002-3796-1601
Henrique TeotónioInstitut de Biologie de l'Ecole Normale Supérieure Paris France.
J Arjan G M de VisserLaboratory of Genetics Wageningen University & Research Wageningen The Netherlands.ORCID https://orcid.org/0000-0002-5098-6686
Marcel E VisserDepartment of Animal Ecology Netherlands Institute of Ecology (NIOO-KNAW) Wageningen The Netherlands.
Renske M A VroomansOrigins Center Groningen The Netherlands.ORCID https://orcid.org/0000-0002-1353-797X
Gijsbert D A WernerDepartment of Zoology University of Oxford Oxford UK.
Bregje WertheimGroningen Institute for Evolutionary Life Sciences University of Groningen Groningen The Netherlands.ORCID https://orcid.org/0000-0001-8555-1925
Pleuni S PenningsSan Francisco State University San Francisco California USA.ORCID https://orcid.org/0000-0001-8704-6578

Funding

Influence of SIV replication on TB progression and immunityR01AI134195 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI AMBROSE, ZANDREA, LIN, PHILANA LING · 2017 to 2021
$4.8M
Microbial Adaptation and the Statistics of Epistasis and PleiotropyR01GM104239 · NIGMS · HARVARD UNIVERSITY · PI DESAI, MICHAEL M · 2013 to 2025
$4.5M
NIAID NIH HHS R01 AI134195NIGMS NIH HHS R01 GM104239
6 · The paper itself

Abstract

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Indexed as

disease modellingevolutionevolutionary controlmodelspopulation geneticspredictabilityprediction

Identifiers

PMID36699126
PMCPMC9850016

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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.