ArticlePLoS computational biology2017
The genotype-phenotype map of an evolving digital organism.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 45 citations in OpenAlex.
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- Six million years of vole dental evolution shaped by tooth development.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evolution takes multiple paths to evolvability when facing environmental change.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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- Bias in the arrival of variation can dominate over natural selection in Richard Dawkins's biomorphs.PLoS computational biology · 2024Article
- Open-endedness in synthetic biology: A route to continual innovation for biological design.Science advances · 2024Review
- The phenotypic plasticity of an evolving digital organism.Royal Society open science · 2022Article
- Functional network motifs defined through integration of protein-protein and genetic interactions.PeerJ · 2022Article
- Insertions and deletions in the RNA sequence-structure map.Journal of the Royal Society, Interface · 2021Article
- On the evolution and development of morphological complexity: A view from gene regulatory networks.PLoS computational biology · 2021Article
- Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis.PLoS computational biology · 2020Article
- Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.Journal of the Royal Society, Interface · 2020Article
- Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure.BMC evolutionary biology · 2019Article
- Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits.Systematic biology · 2019Article
- Protein ensembles link genotype to phenotype.PLoS computational biology · 2019Review
- The architecture of an empirical genotype-phenotype map.Evolution; international journal of organic evolution · 2018Article
- Adding levels of complexity enhances robustness and evolvability in a multilevel genotype-phenotype map.Journal of the Royal Society, Interface · 2018Article
- Non-adaptive origins of evolutionary innovations increase network complexity in interacting digital organisms.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.
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Registered trials
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.