Simultaneous quantification of dynamic bacterial deformation and motility by machine learning
Abstract
We developed a convolutional neural network-based machine learning technique to simultaneously analyze the morphology and motility of spirochetal bacteria swimming with continuous cellular deformation. Matching probabilities between experimental images and learned models realizes quantification of cell morphology and association with motility. This method can be applied to diverse transformable cells, offering critical biophysical insights into microbial dynamics.
Fields of Study & Subjects
Micro and Nano Robotics
Bacterial Genetics and Biotechnology
Bacterial biofilms and quorum sensing
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Publication date
- 8 July 2026
- Document type
- Preprint