#Element not reachable in anylogic series
Another is that this robustness leads to the existence of genotype networks, i.e., networks of genotypes that share the same phenotype, and that can be converted into one another by a series of phenotype-preserving small genetic changes (point mutations). One of them is that such systems are to some extent robust to genotypic change.
These studies have revealed a number of commonalities among otherwise very different systems. Genotype-phenotype maps have been studied in multiple biological systems, including proteins and RNA molecules, genome-scale metabolism, as well as biological circuits that regulate gene activity. We refer to the genotype-phenotype map of natural systems as a (mathematical) function from a space of genotypes to a space of phenotypes, which determines how genotypic information specifies phenotypes through processes such as protein folding and embryonic development. However, there is no universal definition of the genotype-phenotype map. The concept of genotype-phenotype mapping was introduced by Pere Alberch in 1991 as a framework for integrating genetics and developmental biology. To understand the biases and constraints in the production of novel phenotypes, it is necessary to understand how genotypic change translates into phenotypic change. Most importantly, they affect the likelihood that new and beneficial phenotypes-evolutionary adaptations and innovations-originate in the first place. They thus also constrain the directions of evolutionary change. Specifically, they influence what kind of variation becomes available to natural selection. These processes influence how genotypic variation is translated into phenotypic variation. Second, phenotypes form through complex processes such as protein folding and embryonic development. First, genotypic change causes heritable variation, whereas the phenotypic change it brings forth is the substrate of natural selection. This distinction is important for two main reasons. In natural and artificial systems that undergo Darwinian evolution by random mutation and selection, a central distinction is that between a genotype (the entire set of genetic material or a digital organism’s set of instructions, respectively) and a phenotype (the set of observable traits encoded by the genotype). They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. The likely common cause is robustness to genotypic change. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We consider digital organisms from a vast space of 10 141 genotypes (instruction sequences), which can form 512 different phenotypes.
Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems.
To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change.
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