Particle anisotropy tunes emergent behavior in active colloidal systems

Soft Matter ◽  
2022 ◽  
Author(s):  
Shannon E. Moran ◽  
Isaac R. Bruss ◽  
Philip Shoenhofer ◽  
Sharon C Glotzer
Keyword(s):  
Collective Behavior ◽  
Particle Systems ◽  
Emergent Behavior ◽  
Active Particle ◽  
Colloidal Systems ◽  
Particle Anisotropy

Studies of active particle systems have demonstrated that particle anisotropy can impact the collective behavior of a system. However, systems studied to date have served as one-off demonstrations of concept,...

scholarly journals Pattern formation in active particle systems due to competing alignment interactions

2015 ◽  
Vol 224 (7) ◽  
pp. 1325-1347 ◽  
Author(s):  
R. Großmann ◽  
P. Romanczuk ◽  
M. Bär ◽  
L. Schimansky-Geier
Keyword(s):  
Pattern Formation ◽  
Particle Systems ◽  
Active Particle

scholarly journals Unsupervised manifold learning of collective behavior

2020 ◽  
Author(s):  
Mathew Titus ◽  
George Hagstrom ◽  
James R. Watson
Keyword(s):  
Complex Systems ◽  
Prior Knowledge ◽  
Collective Behavior ◽  
Generative Models ◽  
Emergent Behavior ◽  
New Method ◽  
Collective Behaviors ◽  
State Classification ◽  
Macro Scale ◽  
Fish Schooling

AbstractCollective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.Author summaryMany complex systems in society and nature exhibit collective behavior where individuals’ local interactions lead to system-wide organization. One challenge we face today is to identify and characterize these emergent behaviors, and here we have developed a new method for analyzing data from individuals, to detect when a given complex system is exhibiting system-wide organization. Importantly, our approach requires no prior knowledge of the fashion in which the collective behavior arises, or the macro-scale variables in which it manifests. We apply the new method to an agent-based model and empirical observations of fish schooling. While we have demonstrated the utility of our approach to biological systems, it can be applied widely to financial, medical, and technological systems for example.

scholarly journals Forces in inhomogeneous open active-particle systems

2017 ◽  
Vol 96 (5) ◽  
Author(s):  
Nitzan Razin ◽  
Raphael Voituriez ◽  
Jens Elgeti ◽  
Nir S. Gov
Keyword(s):  
Particle Systems ◽  
Active Particle

scholarly journals An Integrative and Modular Framework to Recapitulate Emergent Behavior in Cell Migration

2020 ◽  
Vol 8 ◽  
Author(s):  
Marina B. Cuenca ◽  
Lucía Canedo ◽  
Carolina Perez-Castro ◽  
Hernan E. Grecco
Keyword(s):  
Cell Migration ◽  
Single Cell ◽  
Cancer Progression ◽  
Collective Behavior ◽  
Cell Movement ◽  
Primary Brain Tumor ◽  
Emergent Behavior ◽  
Integrative Model ◽  
Broad Variety ◽  
Modular Framework

Cell migration has been a subject of study in a broad variety of biological systems, from morphogenetic events during development to cancer progression. In this work, we describe single-cell movement in a modular framework from which we simulate the collective behavior of glioblastoma cells, the most prevalent and malignant primary brain tumor. We used the U87 cell line, which can be grown as a monolayer or spatially closely packed and organized in 3D structures called spheroids. Our integrative model considers the most relevant mechanisms involved in cell migration: chemotaxis of attractant factor, mechanical interactions and random movement. The effect of each mechanism is integrated into the overall probability of the cells to move in a particular direction, in an automaton-like approach. Our simulations fit and reproduced the emergent behavior of the spheroids in a set of migration assays where single-cell trajectories were tracked. We also predicted the effect of migration inhibition on the colonies from simple experimental characterization of single treated cell tracks. The development of tools that allow complementing molecular knowledge in migratory cell behavior is relevant for understanding essential cellular processes, both physiological (such as organ formation, tissue regeneration among others) and pathological perspectives. Overall, this is a versatile tool that has been proven to predict individual and collective behavior in U87 cells, but that can be applied to a broad variety of scenarios.

scholarly journals Boltzmann–Grad asymptotic behavior of collisional dynamics

2020 ◽  
pp. 2130001
Author(s):  
V. I. Gerasimenko ◽  
I. V. Gapyak
Keyword(s):  
Asymptotic Behavior ◽  
Collective Behavior ◽  
Kinetic Equations ◽  
Mathematical Understanding ◽  
Particle Systems ◽  
Collisional Dynamics

This paper discusses some of the latest advances in the mathematical understanding of the nature of kinetic equations that describe the collective behavior of many-particle systems with collisional dynamics.

scholarly journals ASYMPTOTIC FIXED-SPEED REDUCED DYNAMICS FOR KINETIC EQUATIONS IN SWARMING

2013 ◽  
Vol 23 (13) ◽  
pp. 2353-2393 ◽  
Author(s):  
MIHAI BOSTAN ◽  
JOSE ANTONIO CARRILLO
Keyword(s):  
Collective Behavior ◽  
Kinetic Equations ◽  
Magnetic Confinement ◽  
Particle Systems ◽  
Friction Forces ◽  
Vicsek Model ◽  
Expansion Procedure ◽  
Short Range Repulsion ◽  
Formal Expansion ◽  
Limit Models

We perform an asymptotic analysis of general particle systems arising in collective behavior in the limit of large self-propulsion and friction forces. These asymptotics impose a fixed speed in the limit, and thus a reduction of the dynamics to a sphere in the velocity variables. The limit models are obtained by averaging with respect to the fast dynamics. We can include all typical effects in the applications: short-range repulsion, long-range attraction, and alignment. For instance, we can rigorously show that the Cucker–Smale model is reduced to a Vicsek-like model without noise in this asymptotic limit. Finally, a formal expansion based on the reduced dynamics allows us to treat the case of diffusion reducing the Cucker–Smale model with diffusion to the non-normalized Vicsek model as in Ref. 29. This technique follows closely the gyroaverage method used when studying the magnetic confinement of charged particles. The main new mathematical difficulty is to deal with measure solutions in this expansion procedure.

scholarly journals A WELL-POSEDNESS THEORY IN MEASURES FOR SOME KINETIC MODELS OF COLLECTIVE MOTION

2011 ◽  
Vol 21 (03) ◽  
pp. 515-539 ◽  
Author(s):  
J. A. CAÑIZO ◽  
J. A. CARRILLO ◽  
J. ROSADO
Keyword(s):  
Collective Behavior ◽  
Hydrodynamic Limit ◽  
Collective Motion ◽  
Kinetic Equations ◽  
Particle Systems ◽  
Well Posedness ◽  
Mass Transportation ◽  
Short Range Repulsion ◽  
Large Groups ◽  
Continuous Dependence Results

We present existence, uniqueness and continuous dependence results for some kinetic equations motivated by models for the collective behavior of large groups of individuals. Models of this kind have been recently proposed to study the behavior of large groups of animals, such as flocks of birds, swarms, or schools of fish. Our aim is to give a well-posedness theory for general models which possibly include a variety of effects: an interaction through a potential, such as a short-range repulsion and long-range attraction; a velocity-averaging effect where individuals try to adapt their own velocity to that of other individuals in their surroundings; and self-propulsion effects, which take into account effects on one individual that are independent of the others. We develop our theory in a space of measures, using mass transportation distances. As consequences of our theory, we show also the convergence of particle systems to their corresponding kinetic equations, and the local-in-time convergence to the hydrodynamic limit for one of the models.

scholarly journals Unsupervised manifold learning of collective behavior

2021 ◽  
Vol 17 (2) ◽  
pp. e1007811
Author(s):  
Mathew Titus ◽  
George Hagstrom ◽  
James R. Watson
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Generative Models ◽  
Emergent Behavior ◽  
Collective Behaviors ◽  
State Classification ◽  
Learning Techniques ◽  
Macro Scale ◽  
Map Alignment ◽  
Observation Training

Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.

scholarly journals Low rattling: A predictive principle for self-organization in active collectives

Science ◽  
2020 ◽  
Vol 371 (6524) ◽  
pp. 90-95
Author(s):  
Pavel Chvykov ◽  
Thomas A. Berrueta ◽  
Akash Vardhan ◽  
William Savoie ◽  
Alexander Samland ◽  
...  
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Molecular Motor ◽  
Self Organization ◽  
Active Particle ◽  
Dynamical Response ◽  
External Forcing ◽  
Response Properties ◽  
Emergent Order ◽  
And Control

Self-organization is frequently observed in active collectives as varied as ant rafts and molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random while capturing their configuration-dependent response to external forcing. This allows derivation of a Boltzmann-like principle for understanding and manipulating driven self-organization. We validate our predictions experimentally, with the use of shape-changing robotic active matter, and outline a methodology for controlling collective behavior. Our findings highlight how emergent order depends sensitively on the matching between external patterns of forcing and internal dynamical response properties, pointing toward future approaches for the design and control of active particle mixtures and metamaterials.

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