ArticleTheScientificWorldJournal2012
In silico evolution of gene cooption in pattern-forming gene networks.
Article in TheScientificWorldJournal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- On the specificity of gene regulatory networks: How does network co-option affect subsequent evolution?Current topics in developmental biology · 2020Review
- Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites.Parallel processing letters · 2014Article
- Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks.Methods (San Diego, Calif.) · 2013Review
- Modeling the evolution of gene regulatory networks for spatial patterning in embryo development.Procedia computer science · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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