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Inferring Interactions from Spatial Patterns and Trajectories: An Overview from Quasiparticles to Biological Movement

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 2561

Special Issue Editor

Dr. Daniel Campos

E-Mail Website
Guest Editor
Department of Physics, Universitat Autònoma de Barcelona, Campus Bellaterra, 08193 Bellaterra, Spain
Interests: statistical physics; random walks; movement ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For many systems consisting of a group of interacting particles in motion, we often have experimental access only to measure (i) a part of the individual trajectories, and/or (ii) the spatial distribution of the group at particular times, but not the corresponding Hamiltonian/rules of interaction between individuals. Researchers then need to face the inverse problem of how to infer such rules from the partial observations available. There is a wide diversity of systems falling within this category, both physical (e.g., skyrmions, phonons, microbeads, or other quasiparticles or particles partially driven through thermal or external noise) and biological (e.g., in the analysis of single-cell trajectories or animal tracking). Due to the ubiquity of the problem, a large amount of learning/inference methods (based on Bayesian inference, information theory, machine learning, network theory, etc.) have been developed. The present issue is intended to provide both (i) an overview of the present state of the art, and (ii) some of the recent advances, in the field. Ideally, we want to illustrate the utility, range of application and advantages/disadvantages of the existing techniques, in order to promote a fruitful exchange of ideas among researchers from the aforementioned areas.

Dr. Daniel Campos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stochastic trajectories
  • learning
  • statistical inference
  • information theory
  • collective phenomena
  • spatial patterns
  • movement ecology

Published Papers (2 papers)

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Research

17 pages, 1624 KiB  
Article
The Dynamic Spatial Structure of Flocks
by Nicholas J. Russell, Kevin R. Pilkiewicz and Michael L. Mayo
Entropy 2024, 26(3), 234; https://doi.org/10.3390/e26030234 - 7 Mar 2024
Viewed by 1121
Abstract
Studies of collective motion have heretofore been dominated by a thermodynamic perspective in which the emergent “flocked” phases are analyzed in terms of their time-averaged orientational and spatial properties. Studies that attempt to scrutinize the dynamical processes that spontaneously drive the formation of [...] Read more.
Studies of collective motion have heretofore been dominated by a thermodynamic perspective in which the emergent “flocked” phases are analyzed in terms of their time-averaged orientational and spatial properties. Studies that attempt to scrutinize the dynamical processes that spontaneously drive the formation of these flocks from initially random configurations are far more rare, perhaps owing to the fact that said processes occur far from the eventual long-time steady state of the system and thus lie outside the scope of traditional statistical mechanics. For systems whose dynamics are simulated numerically, the nonstationary distribution of system configurations can be sampled at different time points, and the time evolution of the average structural properties of the system can be quantified. In this paper, we employ this strategy to characterize the spatial dynamics of the standard Vicsek flocking model using two correlation functions common to condensed matter physics. We demonstrate, for modest system sizes with 800 to 2000 agents, that the self-assembly dynamics can be characterized by three distinct and disparate time scales that we associate with the corresponding physical processes of clustering (compaction), relaxing (expansion), and mixing (rearrangement). We further show that the behavior of these correlation functions can be used to reliably distinguish between phenomenologically similar models with different underlying interactions and, in some cases, even provide a direct measurement of key model parameters. Full article
(This article belongs to the Special Issue Inferring Interactions from Spatial Patterns and Trajectories: An Overview from Quasiparticles to Biological Movement)
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14 pages, 1121 KiB  
Article
Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis
by Yuan Tao, Yuchen Zhou, Zhicheng Zheng, Xiaokang Lei and Xingguang Peng
Entropy 2023, 25(12), 1639; https://doi.org/10.3390/e25121639 - 9 Dec 2023
Viewed by 874
Abstract
We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the [...] Read more.
We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader–follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data. Full article
(This article belongs to the Special Issue Inferring Interactions from Spatial Patterns and Trajectories: An Overview from Quasiparticles to Biological Movement)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The inferrence of hidden Markov models from trajectories of zebrafish
Authors: Remi Monasson; S. Cocco; G. Debregeas
Affiliation: École Normale SupérieureThis link is disabled., Paris, France

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