Every day, we are confronted with decisions. We want to understand the biological basis of decision making, but doing so by trying to understand the nervous system is an extraordinarily complicated task. We have previously shown that there is reason to believe that bacteria could feasibly act as an analog for neurons in a computational sense. To better understand decision making, we therefore consider the stochastic binary choice to become competent (the ability to uptake extracellular bacteria) in Bacillus subtilis, a well-characterized model bacterium, through stochastic and deterministic mathematical models that are then implemented in a computational agent-based model. Our agent-based model is supplemented by wet-lab work being done to determine the diffusion limitations of an extracellular signal called ComX that helps cells become competent. We hope that this work will help elucidate in a mathematical, computational and biological sense the competence binary choice B. subtilis cells undergo and also provide the foundation for further examination of the similarities between ways in which decisions are made by organisms in the natural world.

I’m participating in SuperUROP because I’d like to spend time on a research project. I have a background in biology and neuroscience, which really complements my EECS/machine learning background and helps me see where biological principles can be applied to the networks I’ll be working on. I’m honestly excited to be working on my project! It’s my first time working without grad/postgrad supervision.