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4 October 2024 Tracking active matter particles with DeepTrack
Patrick Grant, Timo Nieminen, Alexander Stilgoe, Halina Rubinsztein-Dunlop
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Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131180A (2024) https://doi.org/10.1117/12.3027939
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
We analyse the collective behaviours of Escherichia coli (E. coli) active matter. The individual movements of these E. coli can be accurately tracked and examined using a recently developed machine learning software called DeepTrack (Midvedt et al., 2021). This provides greater insight into the chaotic dynamics of E. coli swarms as well as the potential to critically assess current theoretical models. DeepTrack analysis can also be applied in more complex environments including interactions with microstructures made with photolithography. Analysing the movements of E. coli active matter with DeepTrack has promising implications in engineering and biomedical applications.
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Patrick Grant, Timo Nieminen, Alexander Stilgoe, and Halina Rubinsztein-Dunlop "Tracking active matter particles with DeepTrack", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131180A (4 October 2024); https://doi.org/10.1117/12.3027939
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KEYWORDS
Particles
Data modeling
Engineering
Machine learning
Motion detection
Motion models
Optical lithography
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