Biofilms are groups of microorganisms, like bacteria, that stick together and form protective communities using substances they produce called extracellular polymeric substances (EPS). These communities make bacteria stronger and more resistant to threats like immune cells and antibiotics. While biofilms can be problematic when they grow on medical implants or in drains, they can also be beneficial, such as in cleaning up pollution in soil and water.

However, studying biofilms in their natural environments, like soil or rocks, is challenging because these environments are complex and hard to see through. This project uses advanced technology to overcome these challenges. We use high-resolution x-ray imaging (micro-CT) and special contrast agents to get detailed 3D images of biofilms growing in materials like sand and beads. This allows us to measure and analyze the structure and growth of biofilms in detail.

We also use cutting-edge deep learning techniques, like Convolutional Neural Networks and Generative Adversarial Networks, to improve the speed and accuracy of our imaging. These methods help us quickly and reliably distinguish between different materials (biofilm, air, water, and solid) and reduce radiation exposure to prevent damage to the biofilms.

Our research aims to test the idea that biofilms can communicate and move within their environments to better respond to stress, such as a lack of water. By turning on and off specific genetic traits in a type of bacteria called S. oneidensis, we can see how important abilities like movement and communication are for the survival of the biofilm community. This is a topic of emphasis within DOE’s Biological and Environmental Research (BER) program.

In summary, this project combines advanced imaging and deep learning to study biofilms in complex environments, helping us understand how these microbial communities survive and thrive.