Statistical Tests for Force Inference in Heterogeneous Environments

Serov, Alexander S.; Laurent, François; Floderer, Charlotte; Perronet, Karen; Favard, Cyril; Muriaux, Delphine; Westbrook, Nathalie; Vestergaard, Christian; Masson, Jean-Baptiste

doi:10.1038/s41598-020-60220-1

Cited by 14 publications

(11 citation statements)

References 81 publications

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“…These assumptions are typically employed to interpret the inferred drift fields as stemming from effective potential energy landscapes [7,9,13,16,20,26,31,32]. Note however that since equilibrium conditions are not guaranteed in biological systems, inferred potential maps cannot generally be interpreted as potential energies in a strict sense as they may include terms of unknown amplitude due to spatial variations in the diffusivity [33] and terms induced by non-equilibrium energy fluxes. Notwithstanding, the inferred maps may capture biologically relevant information regardless of whether the equilibrium assumption is satisfied or not [9,13,16,20].…”

Section: Modelmentioning

confidence: 99%

Mapping spatio-temporal dynamics of single biomolecules in living cells

et al. 2019

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We present a Bayesian framework for inferring spatio-temporal maps of diffusivity and potential fields from recorded trajectories of single molecules inside living cells. The framework naturally lets us regularise the high-dimensional inference problem using prior distributions in order to obtain robust results. To overcome the computational complexity of inferring thousands of map parameters from large single particle tracking datasets, we developed a stochastic optimisation method based on local mini-batches and parsimonious gradient calculation. We quantified the gain in convergence speed on numerical simulations, and we demonstrated for the first time temporal regularisation and aligned values of the inferred potential fields across multiple time segments. As a proof-of-concept, we mapped the dynamics of HIV-1 Gag proteins involved in the formation of virus-like particles (VLPs) on the plasma membrane of live T cells at high spatial and temporal resolutions. We focused on transient aggregation events lasting only on tenth of the time required for full VLP formation. The framework and optimisation methods are implemented in the TRamWAy open-source software platform for analysing single biomolecule dynamics.

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Section: Modelmentioning

confidence: 99%

Mapping spatio-temporal dynamics of single biomolecules in living cells

et al. 2019

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“…The detection of trapping is challenging and has been the subject of investigation by several authors. A possible strategy, based on an ensemble of trajectories, consists of evaluating trapping domains from the evaluation of local confining force [4][5][6][7][8]. On the side of single trajectory analysis, techniques were based on the maximum square displacement [9][10][11], although they are generally too sensitive to noise and local fluctuations of trajectory dynamics.…”

Section: Introductionmentioning

confidence: 99%

Detecting Transient Trapping from a Single Trajectory: A Structural Approach

Lanoiselée

Grimes

Köszegi

et al. 2021

Entropy

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In this article, we introduce a new method to detect transient trapping events within a single particle trajectory, thus allowing the explicit accounting of changes in the particle’s dynamics over time. Our method is based on new measures of a smoothed recurrence matrix. The newly introduced set of measures takes into account both the spatial and temporal structure of the trajectory. Therefore, it is adapted to study short-lived trapping domains that are not visited by multiple trajectories. Contrary to most existing methods, it does not rely on using a window, sliding along the trajectory, but rather investigates the trajectory as a whole. This method provides useful information to study intracellular and plasma membrane compartmentalisation. Additionally, this method is applied to single particle trajectory data of β2-adrenergic receptors, revealing that receptor stimulation results in increased trapping of receptors in defined domains, without changing the diffusion of free receptors.

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“…We refer the reader to Refs. [63][64][65][66][67] for methods addressing spatial and temporal heterogeneities in the overdamped Langevin equation and to Refs. [38,40] for fBM.…”

Section: Introductionmentioning

confidence: 99%

Learning physical properties of anomalous random walks using graph neural networks

Verdier,

Duval,

Laurent

et al. 2021

Preprint

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Single particle tracking allows probing how biomolecules interact physically with their natural environments. A fundamental challenge when analysing recorded single particle trajectories is the inverse problem of inferring the physical model or class of models of the underlying random walks. Reliable inference is made difficult by the inherent stochastic nature of single particle motion, by experimental noise, and by the short duration of most experimental trajectories. Model identification is further complicated by the fact that main physical properties of random walk models are only defined asymptotically, and are thus degenerate for short trajectories.Here, we introduce a new, fast approach to inferring random walk properties based on graph neural networks (GNNs). Our approach consists in associating a vector of features with each observed position, and a sparse graph structure with each observed trajectory. By performing simulation-based supervised learning on this construct [1], we show that we can reliably learn models of random walks and their anomalous exponents. The method can naturally be applied to trajectories of any length. We show its efficiency in analysing various anomalous random walks of biological relevance that were proposed in the AnDi challenge [2]. We explore how information is encoded in the GNN, and we show that it learns relevant physical features of the random walks. We furthermore evaluate its ability to generalize to types of trajectories not seen during training, and we show that the GNN retains high accuracy even with few parameters. We finally discuss the possibility to leverage these networks to analyse experimental data.

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Statistical Tests for Force Inference in Heterogeneous Environments

Cited by 14 publications

References 81 publications

Mapping spatio-temporal dynamics of single biomolecules in living cells

Mapping spatio-temporal dynamics of single biomolecules in living cells

Detecting Transient Trapping from a Single Trajectory: A Structural Approach

Learning physical properties of anomalous random walks using graph neural networks

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