Froese, Tom, Jorge I. Campos, and Nathaniel Virgo. “An iterated learning approach to the origins of the standard genetic code can help to explain its sequence of amino acid assignments.” In ALIFE 2018: The 2018 Conference on Artificial Life , pp. 137-144. MIT Press, 2018.
URL1 URL2
Artificial life has been developing a behavior-based perspective on the origins of life, which emphasizes the adaptive potential of agent-environment interaction even at that initial stage. So far this perspective has been closely aligned to metabolism-first theories, while most researchers who study life’s origins tend to assign an essential role to RNA. An outstanding challenge is to show that a behavior-based perspective can also address open questions related to the genetic system. Accordingly, we have recently applied this perspective to one of science’s most fascinating mysteries: the origins of the standard genetic code. We modeled horizontal transfer of cellular components in a population of protocells using an iterated learning approach and found that it can account for the emergence of several key properties of the standard code. Here we further investigated the diachronic emergence of artificial codes and discovered that the model’s most frequent sequence of amino acid assignments overlaps significantly with the predictions in the literature. Our explorations of the factors that favor early incorporation into an emerging artificial code revealed two aspects: an amino acid’s relative probability of horizontal transfer, and its relative ease of discriminability in chemical space.