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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2311.16753 |
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| _version_ | 1866914805919514624 |
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| author | Klimek, Anton Mondal, Debasmita Block, Stephan Sharma, Prerna Netz, Roland R. |
| author_facet | Klimek, Anton Mondal, Debasmita Block, Stephan Sharma, Prerna Netz, Roland R. |
| contents | We present a method to differentiate organisms solely by their motion based on the generalized Langevin equation (GLE) and use it to distinguish two different swimming modes of strongly confined unicellular microalgae Chlamydomonas reinhardtii (CR). The GLE is the most general model for active or passive motion of organisms and particles and in particular includes non-Markovian effects, i.e., the trajectory memory of its past. We extract all GLE parameters from individual cell trajectories and perform an unbiased cluster analysis to group them into different classes. For the specific cell population employed in the experiments, the GLE-based assignment into the two different swimming modes works perfectly, as checked by control experiments. The classification and sorting of single cells and organisms is important in different areas, our method that is based on motion trajectories offers wide-ranging applications in biology and medicine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16753 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven classification of individual cells by their non-Markovian motion Klimek, Anton Mondal, Debasmita Block, Stephan Sharma, Prerna Netz, Roland R. Biological Physics We present a method to differentiate organisms solely by their motion based on the generalized Langevin equation (GLE) and use it to distinguish two different swimming modes of strongly confined unicellular microalgae Chlamydomonas reinhardtii (CR). The GLE is the most general model for active or passive motion of organisms and particles and in particular includes non-Markovian effects, i.e., the trajectory memory of its past. We extract all GLE parameters from individual cell trajectories and perform an unbiased cluster analysis to group them into different classes. For the specific cell population employed in the experiments, the GLE-based assignment into the two different swimming modes works perfectly, as checked by control experiments. The classification and sorting of single cells and organisms is important in different areas, our method that is based on motion trajectories offers wide-ranging applications in biology and medicine. |
| title | Data-driven classification of individual cells by their non-Markovian motion |
| topic | Biological Physics |
| url | https://arxiv.org/abs/2311.16753 |