108,225 research outputs found
Optimal Self-Organization
We present computational and analytical results indicating that systems of
driven entities with repulsive interactions tend to reach an optimal state
associated with minimal interaction and minimal dissipation. Using concepts
from non-equilibrium thermodynamics and game theoretical ideas, we generalize
this finding to an even wider class of self-organizing systems which have the
ability to reach a state of maximal overall ``success''. This principle is
expected to be relevant for driven systems in physics like sheared granular
media, but it is also applicable to biological, social, and economic systems,
for which only a limited number of quantitative principles are available yet.Comment: This is the detailled revised version of a preprint on
``Self-Organised Optimality'' (cond-mat/9903319). For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.html and
http://angel.elte.hu/~vicsek
Self-organization of collaboration networks
We study collaboration networks in terms of evolving, self-organizing
bipartite graph models. We propose a model of a growing network, which combines
preferential edge attachment with the bipartite structure, generic for
collaboration networks. The model depends exclusively on basic properties of
the network, such as the total number of collaborators and acts of
collaboration, the mean size of collaborations, etc. The simplest model defined
within this framework already allows us to describe many of the main
topological characteristics (degree distribution, clustering coefficient, etc.)
of one-mode projections of several real collaboration networks, without
parameter fitting. We explain the observed dependence of the local clustering
on degree and the degree--degree correlations in terms of the ``aging'' of
collaborators and their physical impossibility to participate in an unlimited
number of collaborations.Comment: 10 pages, 8 figure
The self-organization of genomes
Menzerath-Altmann law is a general law of human language stating, for instance, that the longer a word, the shorter its syllables. With the metaphor that genomes are words and chromosomes are syllables, we examine if genomes also obey the law. We find that longer genomes tend to be made of smaller chromosomes in organisms from three different kingdoms: fungi, plants, and animals. Our findings suggest that genomes self-organize under principles similar to those of human language.Peer ReviewedPostprint (author's final draft
Self-organization of signal transduction
We propose a model of parameter learning for signal transduction, where the
objective function is defined by signal transmission efficiency. We apply this
to learn kinetic rates as a form of evolutionary learning, and look for
parameters which satisfy the objective. This is a novel approach compared to
the usual technique of adjusting parameters only on the basis of experimental
data. The resulting model is self-organizing, i.e. perturbations in protein
concentrations or changes in extracellular signaling will automatically lead to
adaptation. We systematically perturb protein concentrations and observe the
response of the system. We find compensatory or co-regulation of protein
expression levels. In a novel experiment, we alter the distribution of
extracellular signaling, and observe adaptation based on optimizing signal
transmission. We also discuss the relationship between signaling with and
without transients. Signaling by transients may involve maximization of signal
transmission efficiency for the peak response, but a minimization in
steady-state responses. With an appropriate objective function, this can also
be achieved by concentration adjustment. Self-organizing systems may be
predictive of unwanted drug interference effects, since they aim to mimic
complex cellular adaptation in a unified way.Comment: updated version, 13 pages, 4 figures, 3 Tables, supplemental tabl
Self-organization on surfaces: foreword
After decades of work, the growth of continuous thin films, i.e.,
two-dimensional structures, is progressively becoming a technological issue
more than a field of fundamental research. Incidentally self-organization of
nanostructures on surfaces is now an important field of research, i.e.,
structures of dimensionality one or zero, with a steep rise of attention in the
past five years. Whereas self-organization was initially motivated by potential
applications, it has up to now essentially contributed to the advancement of
fundamental science in low dimensions, as model systems could be produced that
could not have been fabricated by lithography. This Special Issue aims at
giving a cross-community timely overview of the field. The Issue gathers a
broad panel of articles covering various self-organization mechanisms, specific
structural characterization, physical properties, and current trends in
extending the versatility of growth. The materials mostly covered here are
semiconductors and magnetic materials.Comment: Foreword of the Editor to Special Issue on Self-organization on
surface
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