Title of article :
Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones
Author/Authors :
Ivan Tanev and Katsunori Shimohara ، نويسنده , , Kikuo Yuta، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Pages :
13
From page :
4469
To page :
4481
Abstract :
We present the results of our work in simulating the recently discovered findings in molecular biology regarding the significant role which histones play in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in evolutionary computation from the commonly considered model of DNA to chromatin (DNA and histones), we present epigenetic programming as an approach, incorporating an explicitly controlled gene expression through modification of histones in strongly-typed genetic programming (STGP). We propose a double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell corresponds to the phylogenesis of the individuals. The beneficial effect of EL on the performance characteristics of STGP is verified on evolution of social behavior of a team of predator agents in the predator–prey pursuit problem. Empirically obtained performance evaluation results indicate that EL contributes to about 2-fold improvement of computational effort of STGP. We trace the cause for that to the cumulative effect of polyphenism and epigenetic stability, both contributed by EL. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.
Keywords :
Genetic programming , Epigenesis , Learning , Histone code
Journal title :
Information Sciences
Serial Year :
2008
Journal title :
Information Sciences
Record number :
1213467
Link To Document :
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