2021
Probing Bacterial Parasitism Using Multi-Agent Reinforcement Learning
Bacteria in their natural environment do not exist in isolation but with several other species. Much richer dynamics can be observed with additional species, such as commensalism and parasitism, which cannot be observed with a single species. In this work, we used multi-agent reinforcement learning to find the optimal policies of these bacterial agents in different environments. In particular, we try to quantify and understand the optimal level of antagonism for a given environment.
2020
Tuning Spatial Profiles of Selection Pressure to Modulate the Evolution of Drug Resistance
Understanding microbial antibiotic resistance is a daunting task, as bacteria grow in complex environments with a myriad of different environmental parameters that can affect their growth rate, such as pH, temperature, density, drug distribution, and nutrient distribution, just to name a few. In many laboratory experiments of bacteria, many of these factors are carefully controlled for to allow scientists to isolate the effects of the quantities of interest. However, can spatial variation in these often-neglected quantities impact the emergence of antibiotic resistance?
