Efficient agent-based models for non-genomic evolution

Modeling dynamical systems composed of aggregations of primitive proteins is critical to the field of astrobiological science, which studies early evolutionary structures dealing with the origins of life. Current theories on the emergence of early life focus either on RNA-world models [2, 3] or protein world models [4, 5]. Traditional modeling based on either model are generally either too slow to converge or too simplified to provide good tools for exploring the trade-offs in the early stages of the emergence of life. This paper focuses on protein-world models and discusses how to model protein aggregations through a utility based multi-agent system. We define agents to control specific properties of a given set of proteins. These properties determine the dynamics of the system, such as the ability for proteins to join or split apart, while additional properties determine the aggregation's fitness as a viable primitive cell. We show that over a wide range of starting conditions, there are mechanisms that allow protein aggregations to achieve high values of overall fitness. In addition through the use of agent-specific utilities that remain aligned with the overall global utility, we are able to reach these conclusions with 50 times fewer learning steps.