Key Projects

1. Collective Honeycomb Construction

The wax-made comb of honeybees is a masterpiece of animal distributed construction. As bees build their nests in preexisting tree cavities, they grow accustomed to dealing with geometric constraints, resulting in non-regular hexagons and topological defects. We study how bees collectively adapt to their environment by 3D-printing experimental frames with a variety of constraints imposed on the imprinted foundations. The combs constructed by the bees show clear evidence of recurring patterns and can be modeled and replicated through a computational model of crystallographic lattice formation. Our interwoven experimental-modeling framework can be used as a first step to describe specific strategies that bees use to effectively solve geometric mismatches while minimizing cost of comb building.

Learn about the details of this work through,

  1. Our PNAS article
  2. PNAS Science Sessions podcast
  3. My talk at APS March Meeting 2022

2. Collective Food Exchange

The mutual exchange and direct transfer of liquid food among eusocial insects such as ants, termites, wasps, and bees is called trophallaxis. This process allows efficient dissemination of nutrients and is crucial for the colony’s survival. We use an agent-based approach to build a trophallaxis-based model, tracking the patterns of the individual interactions and the overall food distribution. We then use experiments with honeybees to monitor their natural food exchange behavior, verify our simulation results and modify the rules of the model to make it more useful. Our experimental results show that honeybees mostly aggregate to exchange food. Based on these experimental observations and analysis, we add attraction range parameter to modify our agents’ movement rules in the model. We found that considering short-range attractions among agents can indeed enhance the overall efficiency of the food distribution for all the turning angle distributions. We are currently applying a range of tools from topological data analysis to deep learning, to enhance our understanding of the underlying patterns and dynamics of this behavior.

Learn about the details of this work through,

  1. Our paper in ALife 2020
  2. SIAM News article
  3. Our poster at the IMSI Conference on Topological Data Analysis (2021)