The phenotypic plasticity of an evolving digital organism

avidaR: an R library to perform complex queries on an ontology-based database of digital organisms

Digital evolution is a branch of artificial life in which self-replicating computer programs-digital organisms-mutate and evolve within a user-defined computational environment. In spite of its value in biology, we still lack an up-to-date and comprehensive database on digital organisms resulting from evolution experiments. Therefore, we have developed an ontology-based semantic database-avidaDB-and an R package-avidaR-that provides users of the R programming language with an easy-to-use tool for performing complex queries without specific knowledge of SPARQL or RDF. avidaR can be used to do research on robustness, evolvability, complexity, phenotypic plasticity, gene regulatory networks, and genomic architecture by retrieving the genomes, phenotypes, and transcriptomes of more than a million digital organisms available on avidaDB. avidaR is already accepted on CRAN (i.e., a comprehensive collection of R packages contributed by the R community) and will make biologists better equipped to embrace the field of digital evolution.

The phenotypic plasticity of an evolving digital organism

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.