The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
The evolutionary origins of modularity
Open accessAbstract
A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.
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Cites
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Cited by
Deep learning in neural networks: an overview
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Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334
Communication dynamics in complex brain networks
Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.149 · Cited by 856
Polyploidy: an evolutionary and ecological force in stressful times
Van de Peer, Y., Ashman, T.-L., Soltis, P. S., & Soltis, D. E. (2021) · The Plant Cell · doi:10.1093/plcell/koaa015 · Cited by 732
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Self-citation
Metadata
- Authors
- Clune, J., Mouret, J.-B., & Lipson, H.
- Venue
- Proceedings of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2013-03-22
- Volume
- 280
- Issue
- 1755
- Pages
- 20122863-20122863
- ISSN
- 0080-4649 (ISSN-L)
- 0962-8452
- 1471-2954
- OpenAlex
- W2097351860
- Cited by
- 466
- References
- 40
- FWCI
- 25.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
