Evolution of Evolvability in Gene Regulatory Networks
Open accessAbstract
Gene regulatory networks are perhaps the most important organizational level in the cell where signals from the cell state and the outside environment are integrated in terms of activation and inhibition of genes. For the last decade, the study of such networks has been fueled by large-scale experiments and renewed attention from the theoretical field. Different models have been proposed to, for instance, investigate expression dynamics, explain the network topology we observe in bacteria and yeast, and for the analysis of evolvability and robustness of such networks. Yet how these gene regulatory networks evolve and become evolvable remains an open question. An individual-oriented evolutionary model is used to shed light on this matter. Each individual has a genome from which its gene regulatory network is derived. Mutations, such as gene duplications and deletions, alter the genome, while the resulting network determines the gene expression pattern and hence fitness. With this protocol we let a population of individuals evolve under Darwinian selection in an environment that changes through time. Our work demonstrates that long-term evolution of complex gene regulatory networks in a changing environment can lead to a striking increase in the efficiency of generating beneficial mutations. We show that the population evolves towards genotype-phenotype mappings that allow for an orchestrated network-wide change in the gene expression pattern, requiring only a few specific gene indels. The genes involved are hubs of the networks, or directly influencing the hubs. Moreover, throughout the evolutionary trajectory the networks maintain their mutational robustness. In other words, evolution in an alternating environment leads to a network that is sensitive to a small class of beneficial mutations, while the majority of mutations remain neutral: an example of evolution of evolvability.
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Mutations in amino acid catabolic processes and stress response sigma factor rpoS are of special importance for acquiring a selective advantage in nutrient starved conditions.
“Mutations in amino acid catabolic processes and stress response sigma factor, rpoS, are of special importance for acquiring a selective advantage in such nutrient starved conditions [148,149];”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [149]
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- Authors
- Crombach, A., & Hogeweg, P.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Crombach, Anton
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-07-11
- Volume
- 4
- Issue
- 7
- Pages
- e1000112
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2432032
- OpenAlex
- W1973081915
- Cited by
- 256
- References
- 40
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 14 · 2023: 10 · 2022: 8 · 2021: 10 · 2020: 10 · 2019: 18 · 2018: 13 · 2017: 8 · 2016: 19 · 2015: 20 · 2014: 13 · 2013: 18 · 2012: 25
- FWCI
- 8.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- gene regulatory networks, evolvability, network evolution, gene duplication, gene deletion, mutational robustness, genotype-phenotype mapping, gene expression dynamics, changing environment, network hubs, beneficial mutations
- MeSH terms
- Adaptation, Biological, Computer Simulation, Gene Expression Regulation, Genotype, Models, Genetic, Mutation, Phenotype, Neural Networks, Computer, Evolution, Molecular, Gene Regulatory Networks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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