Journal article icon

Journal article : Review

Model genotype–phenotype mappings and the algorithmic structure of evolution

Abstract:
Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype–phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.
Publication status:
Published
Peer review status:
Peer reviewed

Actions

Access Document

Publisher copy:
10.1098/rsif.2019.0332

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
ORCID:
0000-0003-2662-1836
More by this author
Role:
Author
ORCID:
0000-0003-0143-9587
More by this author
Role:
Author
ORCID:
0000-0002-2536-4383
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
ORCID:
0000-0003-4333-8506


More from this funder
Funder identifier:
10.13039/100000054
Grant:
U01CA23238


Publisher:
The Royal Society
Journal:
Journal of the Royal Society Interface More from this journal
Volume:
16
Issue:
160
Pages:
20190332
Article number:
20190332
Publication date:
2019-11-06
Acceptance date:
2019-10-04
DOI:
EISSN:
1742-5662
ISSN:
1742-5689


Language:
English
Keywords:
Subtype:
Review
Pubs id:
1072539
Local pid:
pubs:1072539
Source identifiers:
3793607
Deposit date:
2026-02-25
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP