Genotype Editing and the Evolution of Regulation and Memory Luis M. Rocha and Jasleen Kaur School of Informatics, Indiana University Bloomington, IN 47406, USA rocha@indiana.edu http://informatics.indiana.edu/rocha Abstract. Our agent-based model of genotype editing is defined by two distinct genetic components: a coding portion encoding phenotypic solu- tions, and a non-coding portion used to edit the coding material. This set up leads to an indirect, stochastic genotype/phenotype mapping which captures essential aspects of RNA editing. We show that, in drastically changing environments, genotype editing leads to qualitatively different solutions from those obtained via evolutionary algorithms that only use coding genetic material. In particular, we show how genotype editing leads to the emergence of regulatory signals, and also to a resilient mem- ory of a previous environment 1 Introduction: RNA Editing RNA Editing [Bass, 2001] refers to the post-transcriptional alteration of genetic information. It occurs in various forms such as insertion, deletion, or substitution. It can be implemented via non-coding RNAs (ncRNAs) such as guide RNA’s or via enzymes (e.g. adenosine deaminase acting on RNA (ADAR), also known as RNA Editase ) In either case, genetic information is altered after transcription and before translation (for an overview see [Huang et al., 2007]). Previously we quantitatively established the advantages of genotype edit- ing against the canonical evolutionary algorithm in various static and dynamic environments (e.g. [Huang et al., 2007]). Here, using our Agent-Based Model of Genotype Editing (section 2) in drastically changing environments (section 3), we focus instead on the qualitatively different evolutionary solutions attainable via genotype editing. Specifically, we show how genotype editing leads to the emer- gence of regulatory signals that allow agents to better adapt to radically different environments (section 4). We also show how the inclusion of non-coding genetic material, with the function of editing coding material, allows agents to evolve a memory of previous environments—a capacity not attainable by the canonical evolutionary algorithms which use only coding genetic material (section 5). 2 Modeling Genotype Editing The Genetic Algorithm (GA) [Holland, 1975] is an idealized model of natural selection—and the canonical evolutionary algorithm. In a traditional GA, the code between genotype and phenotype is a direct and unique mapping . In bi- ology, however, before a gene is translated into a protein it may be altered, namely by functional or non-coding RNA (ncRNA) used for editing or other regulatory functions. To study and exploit the biological principle behind RNA L.M. Rocha and J. Kaur [2007]."Genotype Editing and the Evolution of Regulation and Memory". Proceedings of the 9th European Conference on Artificial Life. LNAI, Springer, 4648: 63-73.