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Detalle de: The phenotypic plasticity of an evolving digital organism

Title The phenotypic plasticity of an evolving digital organism
Abstract

Climate change will fundamentally reshape life on Earth in the coming decades. Therefore, understanding the extent to which species will cope with rising temperatures is of paramount importance. Phenotypic plasticity is the ability of an organism to change the morphological and functional traits encoded by its genome in response to the environment. I show here that plasticity pervades not only natural but also artificial systems that mimic the developmental process of biological organisms, such as self-replicating and evolving computer programs-digital organisms. Specifically, the environment can modify the sequence of instructions executed from a digital organism's genome (i.e. its transcriptome), which results in changes in its phenotype (i.e. the ability of the digital organism to perform Boolean logic operations). This genetic-based pathway for plasticity comes at a fitness cost to an organism's viability and generation time: the longer the transcriptome (higher fitness cost), the more chances for the environment to modify the genetic execution flow control, and the higher the likelihood for the genome to encode novel phenotypes. By studying to what extent a digital organism's phenotype is influenced by both its genome and the environment, I make a parallelism between natural and artificial evolving systems on how natural selection might slide trait regulation anywhere along a continuum from total environmental control to total genomic control, which harbours lessons not only for designing evolvable artificial systems, but also for synthetic biology.

Apa Reference Fortuna, M. A. (2022). The phenotypic plasticity of an evolving digital organism. Royal Society Open Science, 9(9). 10.1098/rsos.220852
Doi 10.1098/rsos.220852
Publication Date Sep 2022
Publication Type Article
Researcher Miguel Angel Fortuna
Ontology Transcriptomics, Genomics, Parallel Computing
Research Groups Computational Biology at EBD
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