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Evolutionary models in theoretical biology at large, and computational biology and artificial life in particular, rarely deal with ontogenetic, non-inherited alteration of genetic information because they are based on a direct genotype-phenotype mapping. In contrast, in Nature several processes have been discovered which alter genetic information encoded in DNA before it is translated into amino-acid chains. Ontogenetically altered genetic information is not inherited but extensively used in regulation and development of phenotypes, giving organisms the ability to, in a sense, re-program their genotypes according to environmental clues. An example of post-transcriptional alteration of gene-encoding sequences is the process of RNA Editing. Our latest agent-based model of genotype editing presents a novel architecture for evolving agents in which coding and non-coding genetic components are allowed to coevolve. Our goal is twofold: (1) to study the role of RNA Editing regulation in the evolutionary process, and (2) to investigate the conditions under which genotype edition improves the optimization performance of evolutionary algorithms. We have shown that genotype edition allows evolving agents to perform better in several classes of fitness functions, both in static and dynamic environments. We are also investigating the ways in which the indirect genotype/phenotype mapping resulting from genotype editing lead to a better exploration/exploitation compromise in the search process. In the past year we developed an entirely new modeling platform in Python to run experiments to explore the evolutionary advantages of RNA editing.

Some characteristics of our model of RNA Editing:

Genome contains both coding and non- coding portions: Codome and Editome (Editosome)

• Agents with editome perform better in changing environments

Study of regulation via non-coding DNA

• Observe emergence of regulation with promoter signals

• Memory of previous environments

Bio-inspired algorithm for optimization

• Outperfoms traditional evolutionary algorithms on many classes of functions

This research is described in greater detail in the separate Evolutionary Models of Genotype Editing page.

Project Members

Selected Project Publications

• 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. Lecture Notes in Artificial Intelligence (LNAI), 4648: 63-73 (Springer-Verlag).

• C. Huang, J. Kaur, A. Maguitman, L.M. Rocha[2007].”Agent-Based Model of Genotype Editing“. Evolutionary Computation, 15(3): 253-89.

• Rocha, L.M., A. Maguitman, C. Huang, J. Kaur, and S. Narayanan. [2006].”“An Evolutionary Model of Genotype Editing“. In: Artificial Life 10: Tenth International Conference on the Simulation and Synthesis of Living SystemsL.M.Rocha, L. Yaeger, M. Bedau, D. Floreano, R. Goldstone, and A. Vespignani (Eds.). MIT Press, In Press.