Data-driven classification of individual cells by their non-Markovian motion

Klimek, A and Mondal, D and Block, S and Sharma, P and Netz, RR (2024) Data-driven classification of individual cells by their non-Markovian motion. In: Biophysical Journal, 123 (10).

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Abstract

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. The GLE is a general model for active or passive motion of organisms and particles that can be derived from a time-dependent general many-body Hamiltonian 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, which is based on motion trajectories, offers wide-ranging applications in biology and medicine. © 2024 Biophysical Society

Item Type:	Journal Article
Publication:	Biophysical Journal
Publisher:	Biophysical Society
Additional Information:	The copyright for this article belongs to authors.
Department/Centre:	Others Division of Physical & Mathematical Sciences > Physics
Date Deposited:	28 Aug 2024 05:53
Last Modified:	28 Aug 2024 05:53
URI:	http://eprints.iisc.ac.in/id/eprint/84877

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