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Data-driven classification of single cells by their non-markovian motion

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MMVW04 - Modelling non-Markov Movement Processes

The motion of organisms is well known to be a non-equilibrium process, which exhibits drastically different features for different types of organisms. From an evolutionary perspective one can interpret organisms' patterns of motion as adaptions to search efficiently for resources [Klimek & Netz EPL 2022 ] and it is known that cell motion can exhibit non-Markovian motion, which be described by the generalized Langevin equation (GLE) [Mitterwallner et al. PRE 2020 ]. Here, we present a method to differentiate cells solely by their non-Markovian trajectories based on the GLE in a non-equilibrium framework and apply it to distinguish two different swimming types of strongly confined microalgae Chlamydomonas reinhardtii cells with an accuracy of 100%. The model we use is suggested by the data and succeeds to describe the motion on the single cell level. By a simple fit we can extract model parameters for individual cells and subsequently perform an unbiased cluster analysis to determine the number of different cell types in the population and obtain an assignment of every cell to one of the types. Additionally, the model suggested by the data includes information on the underlying processes leading to the observed patterns of motion, which in the case of our Chlamydomonas reinhardtii data hints towards a harmonic coupling inside of the cell. As it still remains a challenge to classify cells on the single cell level, the presented method to distinguish cells with as little information as their trajectories might have important implications in biology and medicine.

This talk is part of the Isaac Newton Institute Seminar Series series.

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