In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks
Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Us...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/2012/560101 |
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| author | Alexander V. Spirov Marat A. Sabirov David M. Holloway |
| author_facet | Alexander V. Spirov Marat A. Sabirov David M. Holloway |
| author_sort | Alexander V. Spirov |
| collection | DOAJ |
| description | Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability—a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters. |
| format | Article |
| id | doaj-art-8ef9fedf2e5943e79765638782755cd1 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-8ef9fedf2e5943e79765638782755cd12025-02-03T05:43:36ZengWileyThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/560101560101In Silico Evolution of Gene Cooption in Pattern-Forming Gene NetworksAlexander V. Spirov0Marat A. Sabirov1David M. Holloway2Computer Science and CEWIT, SUNY Stony Brook, 1500 Stony Brook Road, Stony Brook, NY 11794, USALaboratory of Evolutionary Modeling, The Sechenov Institute of Evolutionary Physiology and Biochemistry, Thorez Prospect 44, Saint Petersburg 2194223, RussiaMathematics Department, British Columbia Institute of Technology, 3700 Willingdon Avenue, Burnaby, BC, V5G 3H2, CanadaGene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability—a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters.http://dx.doi.org/10.1100/2012/560101 |
| spellingShingle | Alexander V. Spirov Marat A. Sabirov David M. Holloway In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks The Scientific World Journal |
| title | In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks |
| title_full | In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks |
| title_fullStr | In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks |
| title_full_unstemmed | In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks |
| title_short | In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks |
| title_sort | in silico evolution of gene cooption in pattern forming gene networks |
| url | http://dx.doi.org/10.1100/2012/560101 |
| work_keys_str_mv | AT alexandervspirov insilicoevolutionofgenecooptioninpatternforminggenenetworks AT maratasabirov insilicoevolutionofgenecooptioninpatternforminggenenetworks AT davidmholloway insilicoevolutionofgenecooptioninpatternforminggenenetworks |