Cooperative self-organization of microorganisms

Ben‐Jacob, Eshel; Cohen, Ilan; Levine, Herbert

doi:10.1080/000187300405228

Cited by 569 publications

(533 citation statements)

References 229 publications

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“…Such phenomena span all ranges of size, scale and number of constituent group members, spread among almost all environments. The striking similarities in observed patterns, and the finding that under certain conditions very different microscopic mechanisms can lead to the same behaviour at the collective level, have paved the way for the theoretical modelling of collective motion and its high-level properties [1,2]. These studies have revealed the power of self-organization in creating new forms and facilitating functions as a result of individual interactions.…”

Section: Introductionmentioning

confidence: 99%

From behavioural analyses to models of collective motion in fish schools

et al. 2012

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Fish schooling is a phenomenon of long-lasting interest in ethology and ecology, widely spread across taxa and ecological contexts, and has attracted much interest from statistical physics and theoretical biology as a case of self-organized behaviour. One topic of intense interest is the search of specific behavioural mechanisms at stake at the individual level and from which the school properties emerges. This is fundamental for understanding how selective pressure acting at the individual level promotes adaptive properties of schools and in trying to disambiguate functional properties from non-adaptive epiphenomena. Decades of studies on collective motion by means of individual-based modelling have allowed a qualitative understanding of the self-organization processes leading to collective properties at school level, and provided an insight into the behavioural mechanisms that result in coordinated motion. Here, we emphasize a set of paradigmatic modelling assumptions whose validity remains unclear, both from a behavioural point of view and in terms of quantitative agreement between model outcome and empirical data. We advocate for a specific and biologically oriented re-examination of these assumptions through experimental-based behavioural analysis and modelling.

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Section: Introductionmentioning

confidence: 99%

From behavioural analyses to models of collective motion in fish schools

et al. 2012

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“…Under certain kinetic constraints, two-dimensional island growth on metal surfaces during deposition can result in dendritic structures [8]. Also, dendrites have been observed during the formation of cubically ordered silica micelle structures [9] and during the growth of bacterial colonies [10]. Finally, an understanding of dendrite growth may play a key role in elucidating the mechanisms of biological ''anti-freeze'' proteins [11].…”

Section: Introductionmentioning

confidence: 99%

Atomistic and continuum modeling of dendritic solidification

Hoyt

2003

Materials Science and Engineering: R: Reports

397

220

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“…Physical settings such as fluid flow in porous media [1], grain-grain displacement in Hele-Shaw cells [2], fracture dynamics [3], adatom and vacancy islands on crystal surfaces [4], and atomic ledges bordering crystalline facets [5,6] present interfaces that violate the hypothesis of the Cartesian representation. Biological systems are also characterized by an approximate spherical symmetry: bacterial colonies [7], fungi [8], epithelial cells [9], and cauliflowers [10] develop rough surfaces which are not describable from a planar reference frame. Also, different contexts like the technological liquid composite molding [11], geological processes as stromatolite morphogenesis [12], and chemical structures [13] provide examples of interfaces that either become larger as time evolves or have a curved geometry, revealing the broad presence of this phenomenon in the natural world.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of curved interfaces

Escudero

2009

Annals of Physics

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Stochastic growth phenomena on curved interfaces are studied by means of stochastic partial differential equations. These are derived as counterparts of linear planar equations on a curved geometry after a reparametrization invariance principle has been applied. We examine differences and similarities with the classical planar equations. Some characteristic features are the loss of correlation through time and a particular behaviour of the average fluctuations. Dependence on the metric is also explored. The diffusive model that propagates correlations ballistically in the planar situation is particularly interesting, as this propagation becomes nonuniversal in the new regime.

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Cooperative self-organization of microorganisms

Cited by 569 publications

References 229 publications

From behavioural analyses to models of collective motion in fish schools

From behavioural analyses to models of collective motion in fish schools

Atomistic and continuum modeling of dendritic solidification

Dynamics of curved interfaces

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