A framework for large-scale bacterial motility behavior analysis

Big data processing has introduced new ideas in the applications of bacterial analysis in recent years. This paper aims to develop an effective framework to automatically extract quantitative knowledge relating to bacterial motility through processing a sequence of large-scale microscopic images of bacterial movements. It was hypothesized that motile bacteria move according to a conceptual model referred to as the “run and tumble” modes. The tremendous amount of microscopic image sequences contains abundant bacteria motility information. However, the traditional experimental methods to identify the trajectories, and segment them to “run” and “tumble” modes are time consuming, subjective, and are not suitable for large-amount and large-scale data analysis. The proposed framework enables rapid and reliable bacteria motility analysis which is important in pathogenic risk assessment, water treatment, soil and groundwater bio-remediation, etc. Furthermore, the framework can be easily adapted to study the diversity and habitats of bacteria from other experimental microscopic images.