Quantifying and analyzing the network basis of genetic complexity.
Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic comp...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3390359?pdf=render |
| _version_ | 1818506432499154944 |
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| author | Ethan G Thompson Timothy Galitski |
| author_facet | Ethan G Thompson Timothy Galitski |
| author_sort | Ethan G Thompson |
| collection | DOAJ |
| description | Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties. |
| first_indexed | 2024-12-10T22:04:28Z |
| format | Article |
| id | doaj.art-e5ee141f08054fe192ca6c178532275f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T22:04:28Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e5ee141f08054fe192ca6c178532275f2022-12-22T01:31:48ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-0187e100258310.1371/journal.pcbi.1002583Quantifying and analyzing the network basis of genetic complexity.Ethan G ThompsonTimothy GalitskiGenotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.http://europepmc.org/articles/PMC3390359?pdf=render |
| spellingShingle | Ethan G Thompson Timothy Galitski Quantifying and analyzing the network basis of genetic complexity. PLoS Computational Biology |
| title | Quantifying and analyzing the network basis of genetic complexity. |
| title_full | Quantifying and analyzing the network basis of genetic complexity. |
| title_fullStr | Quantifying and analyzing the network basis of genetic complexity. |
| title_full_unstemmed | Quantifying and analyzing the network basis of genetic complexity. |
| title_short | Quantifying and analyzing the network basis of genetic complexity. |
| title_sort | quantifying and analyzing the network basis of genetic complexity |
| url | http://europepmc.org/articles/PMC3390359?pdf=render |
| work_keys_str_mv | AT ethangthompson quantifyingandanalyzingthenetworkbasisofgeneticcomplexity AT timothygalitski quantifyingandanalyzingthenetworkbasisofgeneticcomplexity |