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ABSTRACT: The spontaneous emergence of pattern formation is ubiquitous in nature, often arising as a collective phenomenon from interactions among a large number of individual constituents or sub-systems. Understanding, and controlling, collective behavior is dependent on determining the low-level dynamical principles from which spatial and temporal patterns emerge; a key question is whether different group-level patterns result from all components of a system responding to the same external factor, individual components changing behavior but in a distributed self-organized way, or whether multiple collective states co-exist for the same individual behaviors. Using schooling fish (golden shiners, in groups of 30 to 300 fish) as a model system, we demonstrate that collective motion can be effectively mapped onto a set of order parameters describing the macroscopic group structure, revealing the existence of at least three dynamically-stable collective states; swarm, milling and polarized groups. Swarms are characterized by slow individual motion and a relatively dense, disordered structure. Increasing swim speed is associated with a transition to one of two locally-ordered states, milling or highly-mobile polarized groups. The stability of the discrete collective behaviors exhibited by a group depends on the number of group members. Transitions between states are influenced by both external (boundary-driven) and internal (changing motion of group members) factors. Whereas transitions between locally-disordered and locally-ordered group states are speed dependent, analysis of local and global properties of groups suggests that, congruent with theory, milling and polarized states co-exist in a bistable regime with transitions largely driven by perturbations. Our study allows us to relate theoretical and empirical understanding of animal group behavior and emphasizes dynamic changes in the structure of such groups.
PLoS Computational Biology 02/2013; 9(2):e1002915. · 5.22 Impact Factor
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ABSTRACT: Understanding the organization of collective motion in biological systems is
an ongoing challenge. In this Paper we consider a minimal model of
self-propelled particles with variable speed. Inspired by experimental data
from schooling fish, we introduce a power-law dependency of the speed of each
particle on the degree of polarization order in its neighborhood. We derive
analytically a coarse-grained continuous approximation for this model and find
that, while the variable speed rule does not change the details of the ordering
transition leading to collective motion, it induces an inverse power-law
correlation between the speed or the local polarization order and the local
density. Using numerical simulations, we verify the range of validity of this
continuous description and explore regimes beyond it. We discover, in
disordered states close to the transition, a phase-segregated regime where most
particles cluster into almost static groups surrounded by isolated high-speed
particles. We argue that the mechanism responsible for this regime could be
present in a wide range of collective motion dynamics.
02/2012;
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ABSTRACT: Determining individual-level interactions that govern highly coordinated motion in animal groups or cellular aggregates has been a long-standing challenge, central to understanding the mechanisms and evolution of collective behavior. Numerous models have been proposed, many of which display realistic-looking dynamics, but nonetheless rely on untested assumptions about how individuals integrate information to guide movement. Here we infer behavioral rules directly from experimental data. We begin by analyzing trajectories of golden shiners (Notemigonus crysoleucas) swimming in two-fish and three-fish shoals to map the mean effective forces as a function of fish positions and velocities. Speeding and turning responses are dynamically modulated and clearly delineated. Speed regulation is a dominant component of how fish interact, and changes in speed are transmitted to those both behind and ahead. Alignment emerges from attraction and repulsion, and fish tend to copy directional changes made by those ahead. We find no evidence for explicit matching of body orientation. By comparing data from two-fish and three-fish shoals, we challenge the standard assumption, ubiquitous in physics-inspired models of collective behavior, that individual motion results from averaging responses to each neighbor considered separately; three-body interactions make a substantial contribution to fish dynamics. However, pairwise interactions qualitatively capture the correct spatial interaction structure in small groups, and this structure persists in larger groups of 10 and 30 fish. The interactions revealed here may help account for the rapid changes in speed and direction that enable real animal groups to stay cohesive and amplify important social information.
Proceedings of the National Academy of Sciences 07/2011; 108(46):18720-5. · 9.68 Impact Factor
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ABSTRACT: In this article we derive the effective pairwise interactions in a Langevin-type united atoms model of water. The interactions are determined from the trajectories of a detailed molecular dynamics simulation of simple point charge water. A standard method is used for estimating the conservative interaction, whereas a new "bottom-up" method is used to determine the effective dissipative and stochastic interactions. We demonstrate that when compared to the standard united atoms model, the transport properties of the coarse-grained model is significantly improved by the introduction of the derived dissipative and stochastic interactions. The results are compared to a previous study, where a "top-down" approach was used to obtain transport properties consistent with those of the simple point charge water model.
The Journal of chemical physics 05/2009; 130(16):164509. · 3.09 Impact Factor
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ABSTRACT: We introduce a method for determining the functional form of the stochastic and dissipative interactions in a dissipative particle dynamics (DPD) model from projected phase space trajectories. The DPD model is viewed as a coarse graining of a detailed dynamics that displays a clear timescale separation. Based on the Mori-Zwanzig projection operator method we derive a consistency equation for the stochastic interaction in DPD. The consistency equation can be solved by an iterative bootstrapping procedure. Combined with standard techniques for estimating the conservative interaction, our method makes it possible to reconstruct all the forces in a coarse-grained DPD model. We demonstrate how the method works by recreating the interactions in a DPD model from its phase space trajectory. Furthermore, we discuss how our method can be used in realistic systems with finite timescale separation.
Journal of Physics Condensed Matter 03/2009; 21(9):095401. · 2.55 Impact Factor
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ABSTRACT: We investigate how the transport properties of a united atom fluid with a dissipative particle dynamics thermostat depend on the functional form and magnitude of both the conservative and the stochastic interactions. We demonstrate how the thermostat strongly affects the hydrodynamics, especially diffusion, viscosity, and local escape times. As model system we use simple point charge (SPC) water, from which projected trajectories are used to determine the effective interactions in the united atom model. The simulation results support our argument that the thermostat should be viewed as an integral part of the coarse-grained dynamics rather than a tool for approaching thermal equilibrium. As our main result we show that the united atom model with the adjusted effective interactions approximately reproduces the diffusion constant and the viscosity of the underlying detailed SPC water model.
The Journal of chemical physics 08/2008; 129(2):024106. · 3.09 Impact Factor
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ABSTRACT: We investigate how the transport properties of a united atoms fluid with a dissipative particle dynamics thermostat depend on the functional form and magnitude of both the conservative and the stochastic interactions. We demonstrate how the thermostat strongly affects the hydrodynamics, especially diffusion, viscosity, and local escape times. As model system we use SPC water, from which projected trajectories are used to determine the effective interactions in the united atoms model. The simulation results support our argument that the thermostat should be viewed as an integral part of the coarse-grained dynamics, rather than a tool for approaching thermal equilibrium. As our main result we show that the united atoms model with the adjusted effective interactions approximately reproduce the diffusion constant and the viscosity of the underlying detailed SPC water model. Comment: 8 pages
02/2008;
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ABSTRACT: There exist methods for determining effective conservative interactions in coarse-grained particle-based mesoscopic simulations. The resulting models can be used to capture thermal equilibrium behavior, but the model system we study does not correctly represent transport properties. We suggest the use of force covariance to determine the full functional form of dissipative and stochastic interactions. We show that a combination of the RDF and a force covariance function can be used to determine all interactions in dissipative particle dynamics (DPD). Furthermore, we use the method to test whether the effective interactions in DPD can be adjusted to produce a force covariance consistent with the projection of a microscopic Lennard-Jones simulation. The results indicate that the DPD ansatz may not be consistent with the underlying microscopic dynamics. We discuss how this result relates to theoretical studies reported in the literature.
Physical Review E 02/2008; 77(1 Pt 2):016707. · 2.26 Impact Factor
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ABSTRACT: This paper is a discussion on how reaction kinetics and three-dimensional (3D) lattice simulations can be used to elucidate the dynamical properties of micelles as a possible minimal protocell container. We start with a general discussion on the role of molecular self-assembly in prebiotic and contemporary biological systems. A simple reaction kinetic model of a micellation process of amphiphilic molecules in water is then presented and solved analytically. Amphiphilic molecules are polymers with hydrophobic (water-fearing), e.g. hydrocarbon tail groups, and hydrophilic (water-loving) head groups, e.g. fatty acids. By making a few simplifying assumptions an analytical expression for the size distribution of the resulting micelles can be derived. The main part of the paper presents and discusses a lattice gas technique for a more detailed 3D simulation of molecular self-assembly of amphiphilic polymers in aqueous environments. Water molecules, hydrocarbon tail groups and hydrophilic head groups are explicitly represented on a three-dimensional discrete lattice. Molecules move on the lattice proportional to their continuous momentum. Collision rules preserve momentum and kinetic energy. Potential energy from molecular interactions are also included explicitly. The non-trivial thermodynamics of large-scale and long-time dynamics are studied. In this paper we specifically demonstrate how, from a random initial distribution, micelles are formed and grow until they destabilize and can divide. Eventually a steady state of growing and dividing micelles is formed. Towards the end of the paper we discuss the relevance of the presented results to the design of a minimal artificial protocell.
International Journal of Astrobiology. 12/2004; 4(01):81 - 91.
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ABSTRACT: In this paper, we introduce a method for determining local interaction rules in animal swarms. The method is based on the assumption that the behavior of individuals in a swarm can be treated as a set of mechanistic rules.The principal idea behind the technique is to vary parameters that define a set of hypothetical interactions, as for example, a rule for aligning with neighbors. The parameter values are optimized so that the deviation between the observed movements in an animal swarm and the movements predicted by the assumed rule set is minimal. We demonstrate the method by reconstructing the interaction rules from the trajectories produced by a computer simulation. Copyright 2010, Oxford University Press.
Behavioral Ecology. 21(5):1106-1111.
