Literature DB >> 28812721

Predicting evolution.

Michael Lässig1, Ville Mustonen2, Aleksandra M Walczak3.   

Abstract

The face of evolutionary biology is changing: from reconstructing and analysing the past to predicting future evolutionary processes. Recent developments include prediction of reproducible patterns in parallel evolution experiments, forecasting the future of individual populations using data from their past, and controlled manipulation of evolutionary dynamics. Here we undertake a synthesis of central concepts for evolutionary predictions, based on examples of microbial and viral systems, cancer cell populations, and immune receptor repertoires. These systems have strikingly similar evolutionary dynamics driven by the competition of clades within a population. These dynamics are the basis for models that predict the evolution of clade frequencies, as well as broad genetic and phenotypic changes. Moreover, there are strong links between prediction and control, which are important for interventions such as vaccine or therapy design. All of these are key elements of what may become a predictive theory of evolution.

Entities:  

Year:  2017        PMID: 28812721     DOI: 10.1038/s41559-017-0077

Source DB:  PubMed          Journal:  Nat Ecol Evol        ISSN: 2397-334X            Impact factor:   15.460


  60 in total

Review 1.  Effective models and the search for quantitative principles in microbial evolution.

Authors:  Benjamin H Good; Oskar Hallatschek
Journal:  Curr Opin Microbiol       Date:  2018-12-06       Impact factor: 7.934

Review 2.  Chromothripsis, a credible chromosomal mechanism in evolutionary process.

Authors:  Franck Pellestor; Vincent Gatinois
Journal:  Chromosoma       Date:  2018-08-07       Impact factor: 4.316

3.  Going, going, gone: predicting the fate of genomic insertions in plant RNA viruses.

Authors:  Anouk Willemsen; José L Carrasco; Santiago F Elena; Mark P Zwart
Journal:  Heredity (Edinb)       Date:  2018-05-10       Impact factor: 3.821

4.  On the deformability of an empirical fitness landscape by microbial evolution.

Authors:  Djordje Bajić; Jean C C Vila; Zachary D Blount; Alvaro Sánchez
Journal:  Proc Natl Acad Sci U S A       Date:  2018-10-15       Impact factor: 11.205

5.  Predicting bacterial promoter function and evolution from random sequences.

Authors:  Mato Lagator; Srdjan Sarikas; Calin C Guet; Gašper Tkačik; Magdalena Steinrueck; David Toledo-Aparicio; Jonathan P Bollback
Journal:  Elife       Date:  2022-01-26       Impact factor: 8.140

6.  Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution.

Authors:  John Huddleston; John R Barnes; Thomas Rowe; Xiyan Xu; Rebecca Kondor; David E Wentworth; Lynne Whittaker; Burcu Ermetal; Rodney Stuart Daniels; John W McCauley; Seiichiro Fujisaki; Kazuya Nakamura; Noriko Kishida; Shinji Watanabe; Hideki Hasegawa; Ian Barr; Kanta Subbarao; Pierre Barrat-Charlaix; Richard A Neher; Trevor Bedford
Journal:  Elife       Date:  2020-09-02       Impact factor: 8.140

7.  Evolution-informed forecasting of seasonal influenza A (H3N2).

Authors:  Xiangjun Du; Aaron A King; Robert J Woods; Mercedes Pascual
Journal:  Sci Transl Med       Date:  2017-10-25       Impact factor: 17.956

8.  Phosphorus limitation does not drive loss of bony lateral plates in freshwater stickleback (Gasterosteus aculeatus).

Authors:  Sophie L Archambeault; Daniel J Durston; Alex Wan; Rana W El-Sabaawi; Blake Matthews; Catherine L Peichel
Journal:  Evolution       Date:  2020-06-22       Impact factor: 3.694

Review 9.  Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology.

Authors:  Dylan H Morris; Katelyn M Gostic; Simone Pompei; Trevor Bedford; Marta Łuksza; Richard A Neher; Bryan T Grenfell; Michael Lässig; John W McCauley
Journal:  Trends Microbiol       Date:  2017-10-30       Impact factor: 17.079

10.  Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing.

Authors:  Yiquan Wang; Ruipeng Lei; Armita Nourmohammad; Nicholas C Wu
Journal:  Elife       Date:  2021-12-08       Impact factor: 8.140

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