Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion on ArXiv

Input graph structure including a redundant number of edges. (Image by J. Pineda.)
Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion
Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe, Carlo Manzo
arXiv: 2202.06355

The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles, and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here, we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically-relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments.

Press release on Objective comparison of methods to decode anomalous diffusion

The article Objective comparison of methods to decode anomalous diffusion has been featured in the News of the University of Gothenburg.

The study, published in Nature Communications and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, originates from the AnDi Challenge, a competition co-organised by Giovanni Volpe with researchers from University of Vic – Central University of Catalunya, Institute of Photonic Sciences in Barcelona, University of Potsdam, and Valencia Polytechnic University.

The challenge was held during March–November 2020 and consisted of three main tasks concerning anomalous exponent inference, model classification, and trajectory segmentation. The goal was to provide an objective assessment of the performance of methods to characterise anomalous diffusion from single trajectories.

Here the links to the press releases:
English: A scientific competition led to improved methods for analysing the diffusion of particles.
Swedish: En vetenskaplig tävling ledde till förbättrade metoder för att analysera diffusion av partiklar.

Objective comparison of methods to decode anomalous diffusion published in Nature Communications

An illustration of anomalous diffusion. (Image by Gorka Muñoz-Gil.)
Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolás Firbas, Òscar Garibo i Orts, Alessia Gentili, Zihan Huang, Jae-Hyung Jeon, Hélène Kabbech, Yeongjin Kim, Patrycja Kowalek, Diego Krapf, Hanna Loch-Olszewska, Michael A. Lomholt, Jean-Baptiste Masson, Philipp G. Meyer, Seongyu Park, Borja Requena, Ihor Smal, Taegeun Song, Janusz Szwabiński, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Arthur Widera, Maciej Lewenstein, Ralf Metzler, and Carlo Manzo
Nat. Commun. 12, Article number: 6253 (2021)
doi: 10.1038/s41467-021-26320-w
arXiv: 2105.06766

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Characterisation of Physical Processes from Anomalous Diffusion Data, special issue on Journal of Physics A

Logo of the AnDi challenge.

Characterisation of Physical Processes from Anomalous Diffusion Data
Guest Editors
Miguel A Garcia-March, Maciej Lewenstein, Carlo Manzo, Ralf Metzler, Gorka Muñoz-Gil, Giovanni Volpe
Journal of Physics A: Mathematical and Theoretical
URL: Special Issue on Characterisation of Physical Processes from Anomalous Diffusion Data

In many systems, stochastic transport deviates from the standard laws of Brownian motion. Determining the exponent α characterising anomalous diffusion and identifying the physical origin of this behaviour are crucial steps to understanding the nature of the systems under observation. However, the determination of these properties from the analysis of the measured trajectories is often difficult, especially when these trajectories are short, irregularly sampled, or switching between different behaviours.

Over the last years, several methods have been proposed to quantify anomalous diffusion and the underlying physical process, going beyond the classical calculation of the mean squared displacement. More recently, the advent of machine learning has produced a boost in the methods to quantify anomalous diffusion.

The AnDi challenge aims at bringing together a vibrating and multidisciplinary community of scientists working on this problem. The use of the same reference datasets will allow an unbiased assessment of the performance of methods for characterising anomalous diffusion from single trajectories. This Special Issue will report on these approaches and their performance.

The deadline for submissions will be 30th June 2021 and you can submit manuscripts through ScholarOne Manuscripts. All papers will be refereed according to the usual high standards of the journal.

AnDi: The Anomalous Diffusion Challenge on ArXiv

Logo of the AnDi challenge

AnDi: The Anomalous Diffusion Challenge
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Ralf Metzler, Maciej Lewenstein & Carlo Manzo
arXiv: 2003.12036

The deviation from pure Brownian motion generally referred to as anomalous diffusion has received large attention in the scientific literature to describe many physical scenarios. Several methods, based on classical statistics and machine learning approaches, have been developed to characterize anomalous diffusion from experimental data, which are usually acquired as particle trajectories. With the aim to assess and compare the available methods to characterize anomalous diffusion, we have organized the Anomalous Diffusion (AnDi) Challenge (http://www.andi-challenge.org/). Specifically, the AnDi Challenge will address three different aspects of anomalous diffusion characterization, namely: (i) Inference of the anomalous diffusion exponent. (ii) Identification of the underlying diffusion model. (iii) Segmentation of trajectories. Each problem includes sub-tasks for different number of dimensions (1D, 2D and 3D). In order to compare the various methods, we have developed a dedicated open-source framework for the simulation of the anomalous diffusion trajectories that are used for the training and test datasets. The challenge was launched on March 1, 2020, and consists of three phases. Currently, the participation to the first phase is open. Submissions will be automatically evaluated and the performance of the top-scoring methods will be thoroughly analyzed and compared in an upcoming article.