Studying the Evolution of Division of Labor with Digital Organisms

This post is by MSU Postdoc Heather Goldsby.

Heather Goldsby, an MSU BEACON postdoctoral researcher working in the Devolab, Kerr, and Hintze labs.

Why do you have different types of cells in your body? Why do honeybees perform different roles, including forager, undertaker, nurse, and queen? Why do factory workers perform jobs as specific as putting on one part for a car? Why do we engineer robots to do different tasks in support of the same mission? All of these are examples of division of labor where individuals take on specific roles and cooperate to survive and thrive. I first became fascinated by division of labor when I realized it underpins all of our economy – and most of our daily interactions at work. My interest only grew as I began to notice it everywhere. Originally, my research started by using division of labor as a tool in algorithms I developed. Then, I started to work on creating evolutionary algorithms that employed division of labor to solve a problem.

As I continued my studies, I learned that division of labor is a key component of major transitions in evolution, and thus of great interest in biology. Major transitions in evolution occur when formerly distinct lower-level entities become linked (either by staying together or by forging bonds) and reproduce and compete as one higher-level entity [1]. For example, major transitions include single cells transitioning into multicellular organisms, solitary insects transitioning into eusocial colonies, and the formation of the eukaryotic cell. For some of these transitions, in particular the ones where genetically similar lower-level entities stay together, a big challenge is how to evolve to specialize and take advantage of the benefits of division of labor.

Because of its importance to science and engineering, I want to better understand how and why division of labor evolves. To do this, I use the Avida digital evolution platform [2]. Its rapid generation times and experimental control enable me to place digital organism (analogous to cells or ants) into groups, apply different evolutionary pressures, and observe how and when division of labor evolves. Using this approach, I’ve studied several aspects of the evolution of division of labor, including: (1) The evolution of temporal polyethism [3]: a form of division of labor used by some bees, where individuals change the task they performed based on their age; (2) The evolution of reproductive division of labor [4]. In particular, we became interested in why do somatic (body) cells evolve when they are evolutionary dead ends? (3) What is the role of task-switching costs in promoting specialization [5]? I’m going to go into greater detail on this question to illustrate how we use digital evolution to study division of labor.

Do task-switching costs promote the evolution of division of labor? Task-switching costs are penalties (in terms of time or resources) associated with changing from performing one type of task to another. For us, they might be the amount of time it takes to shift from checking email or Facebook, to get back to writing a paper. For example, for a bee within a colony, they could include the amount of time it takes to travel from one place in the hive to another, the morphological overhead in changing roles (building new glands, etc.), or even cognitive overhead. To study this question, we placed individual digital organisms into a colony, where the colony as a whole was required to perform a variety of tasks to successfully compete with other colonies. We ran treatments that varied the task-switching costs. We observed that treatments with low task-switching costs evolved generalist organisms: an organism performed many types of tasks. In contrast, when higher task-switching costs were applied, specialist organisms that evolved only one type of task evolved. In an unexpected twist, the specialist individuals also evolved task-partitioning behavior where one individual passed on the results of a task to another individual who used the solution as a building block to perform a more complex task. We see this behavior in both human assembly lines and also insect colonies, such as leaf cutting ants.

Fig. 1. A task-partitioning system evolved by a digital colony. Digital organisms (squares) perform tasks and send messages (solid lines), including task results. In this case, the organisms evolved to send task results to neighbors, who, in turn, used the information to perform more complex tasks.

What fascinated me about the task-partitioning behavior evolved by the individuals within colonies was that while the colony as a whole could perform seven different types of tasks, when placed alone, the individuals could only perform one type of task (Fig. 1). The loss of functionality at the lower-level individual and the emergence of functionality at the level of the colony indicates that task-switching costs could favor both the evolution of division of labor and also a shift in autonomy from a lower-level to a higher-level unit. This shift in autonomy is a fundamental component of major transitions in evolution.

This project highlights how digital evolution can contribute to studies of division of labor and major transitions in evolution. Now, working with colleagues, I’m expanding this research to both understand other evolutionary pressures that favor division of labor and also to see a major transition in evolution unfold in real time.

References:

  1. J. Maynard-Smith and E. Szathmáry, The major transitions in evolution. New York, NY, USA: Oxford University Press, 1997.
  2. C. Ofria and C. O. Wilke, “Avida: A software platform for research in computational evolutionary 
biology,” Journal of Artificial Life, vol. 10, pp. 191–229, 2004.
  3. H. J. Goldsby, N. Serra, F. Dyer, B. Kerr, and C. Ofria, “The evolution of temporal polyethism,” Artificial Life, vol. 13, pp. 178–185, 2012.
  4. H. J. Goldsby, D. B. Knoester, C. Ofria, and B. Kerr, “The evolutionary origin of somatic cells under the dirty work hypothesis,” PLoS Biol, vol. 12, no. 5:e1001858, 2014.
  5. H. J. Goldsby, A. Dornhaus, B. Kerr, and C. Ofria, “Task-switching costs promote the evolution of division of labor and shifts in individuality,” Proceedings of the National Academy of Sciences, vol. 109, no. 34, pp. 13686–13691, 2012.
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