A Digital Evolution Infrastructure for Experimental Investigations into the Evolution of Division of Labor

点击翻译按钮获取中文摘要

Division of labor, where organisms specialize on roles and cooperate to survive, is a strategy employed by bacteria, slime molds, eusocial insects, and even humans. Moreover, major transitions in evolution, where formerly distinct individuals join together in a higher-level unit that functions as a single reproductive entity, also exhibit division of labor. For example, single cells join together and specialize to become a multicellular organism. A fundamental question in biology is why does division of labor evolve? While biologists are fascinated by this question, it remains challenging, if not impossible, to study with natural systems due to imperfections in the historical data and long generation times. This research involves developing a software infrastructure to investigate the evolution of division of labor.Efficiency gains resulting from division of labor are a central component of hypotheses for why division of labor evolves within groups and why major transitions in evolution occur. To understand whether efficiency gains are sufficient to motivate these evolutionary changes, the investigators study populations of digital organisms, which are fully-functional computer programs that evolve in an open-ended manner. In contrast to experimental evolution with natural systems, digital organisms have rapid generation times, unparalleled experimental control, and automated data collection that facilitates explorations of underlying mechanisms. Using the Avida digital evolution platform as a foundation, the researchers are building an infrastructure to investigate topics surrounding the evolution of division of labor, including: (1) the role of learning in the evolution of task-based division of labor, (2) whether an increased cost of reproduction influences when fraternal major transitions occur, and (3) whether specialization capabilities motivate genetically distinct organisms to reproduce as a group.