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## The three different phases in the dynamics of chemical reaction networks and their relationship to cancer

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 04/12/2012
Português

Relevância na Pesquisa

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We investigate the catalytic reactions model used in cell modeling. The
reaction kinetic is defined through the energies of different species of
molecules following random independent distribution. The related statistical
physics model has three phases and these three phases emerged in the dynamics:
fast dynamics phase, slow dynamic phase and ultra-slow dynamic phase. The
phenomenon we found is a rather general, does not depend on the details of the
model. We assume as a hypothesis that the transition between these phases
(glassiness degrees) is related to cancer. The imbalance in the rate of
processes between key aspects of the cell (gene regulation, protein-protein
interaction, metabolical networks) creates a change in the fine tuning between
these key aspects, affects the logics of the cell and initiates cancer. It is
probable that cancer is a change of phase resulting from increased and
deregulated metabolic reactions.; Comment: 5 pages, 2 figures, EPL, in press

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## Inferring dynamic genetic networks with low order independencies

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.62%

In this paper, we propose a novel inference method for dynamic genetic
networks which makes it possible to face with a number of time measurements n
much smaller than the number of genes p. The approach is based on the concept
of low order conditional dependence graph that we extend here in the case of
Dynamic Bayesian Networks. Most of our results are based on the theory of
graphical models associated with the Directed Acyclic Graphs (DAGs). In this
way, we define a minimal DAG G which describes exactly the full order
conditional dependencies given the past of the process. Then, to face with the
large p and small n estimation case, we propose to approximate DAG G by
considering low order conditional independencies. We introduce partial qth
order conditional dependence DAGs G(q) and analyze their probabilistic
properties. In general, DAGs G(q) differ from DAG G but still reflect relevant
dependence facts for sparse networks such as genetic networks. By using this
approximation, we set out a non-bayesian inference method and demonstrate the
effectiveness of this approach on both simulated and real data analysis. The
inference procedure is implemented in the R package 'G1DBN' freely available
from the CRAN archive.

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## Scale-dependent non-affine elasticity of semiflexible polymer networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 31/10/2012
Português

Relevância na Pesquisa

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The cytoskeleton of eukaryotic cells provides mechanical support and governs
intracellular transport. These functions rely on the complex mechanical
properties of networks of semiflexible protein filaments. Recent theoretical
interest has focused on mesoscopic properties of such networks and especially
on the effect of local, non-affine bending deformations on mechanics. Here, we
study the impact of local network deformations on the scale-dependent mobility
of probe particles in entangled networks of semiflexible actin filaments by
high-bandwidth microrheology. We find that micron-sized particles in these
networks experience two opposing non-continuum elastic effects: entropic
depletion reduces the effective network rigidity, while local non-affine
deformations of the network substantially enhance the rigidity at low
frequencies. We show that a simple model of lateral bending of filaments
embedded in a viscoelastic background leads to a scaling regime for the
apparent elastic modulus G'(\omega) \sim \omega^{9/16}, closely matching the
experiments. These results provide quantitative evidence for how different a
semiflexible polymer network can feel for small objects, and they demonstrate
how non-affine bending deformations can be dominant for the mobility of
vesicles and organelles in the cell.; Comment: 14 pages including 3 figures

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## Prosperity is associated with instability in dynamical networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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Social, biological and economic networks grow and decline with occasional
fragmentation and re-formation, often explained in terms of external
perturbations. We show that these phenomena can be a direct consequence of
simple imitation and internal conflicts between 'cooperators' and 'defectors'.
We employ a game-theoretic model of dynamic network formation where successful
individuals are more likely to be imitated by newcomers who adopt their
strategies and copy their social network. We find that, despite using the same
mechanism, cooperators promote well-connected highly prosperous networks and
defectors cause the network to fragment and lose its prosperity; defectors are
unable to maintain the highly connected networks they invade. Once the network
is fragmented it can be reconstructed by a new invasion of cooperators, leading
to the cycle of formation and fragmentation seen, for example, in bacterial
communities and socio-economic networks. In this endless struggle between
cooperators and defectors we observe that cooperation leads to prosperity, but
prosperity is associated with instability. Cooperation is prosperous when the
network has frequent formation and fragmentation.; Comment: 49 pages, 10 figures; Journal of Theoretical Biology (2011)

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## Exploration of Network Scaling: Variations on Optimal Channel Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/10/2012
Português

Relevância na Pesquisa

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#Mathematics - Optimization and Control#Physics - Biological Physics#Quantitative Biology - Quantitative Methods

Metabolic allometry, a common pattern in nature, is a close-to-3/4-power
scaling law between metabolic rate and body mass in organisms, across and
within species. An analogous relationship between metabolic rate and water
volume in river networks has also been observed. Optimal Channel Networks
(OCNs), at local optima, accurately model many scaling properties of river
systems, including metabolic allometry. OCNs are embedded in two-dimensional
space; this work extends the model to three dimensions. In this paper we
compare characteristics of 3d OCNs with 2d OCNs and with organic metabolic
networks, studying the scaling behaviors of area, length, volume, and energy.
In addition, we take a preliminary look at comparing Steiner trees with OCNs.
We find that the three-dimensional OCN has predictable characteristics
analogous to those of the two-dimensional version, as well as scaling
properties similar to metabolic networks in biological organisms.; Comment: 15 pages

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## Extracting Hidden Hierarchies in 3D Distribution Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 15/10/2014
Português

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Natural and man-made transport webs are frequently dominated by dense sets of
nested cycles. The architecture of these networks, as defined by the topology
and edge weights, determines how efficiently the networks perform their
function. Yet, the set of tools that can characterize such a weighted
cycle-rich architecture in a physically relevant, mathematically compact way is
sparse. In order to fill this void, we have developed a new algorithm that
rests on an abstraction of the physical `tiling' in the case of a two
dimensional network to an effective tiling of an abstract surface in space that
the network may be thought to sit in. Generically these abstract surfaces are
richer than the flat plane and as a result there are now two families of
fundamental units that may aggregate upon cutting weakest links -- the
plaquettes of the tiling and the longer `topological' cycles associated with
the abstract surface itself. Upon sequential removal of the weakest links, as
determined by the edge weight, neighboring plaquettes merge and a tree
characterizing this merging process results. The properties of this
characteristic tree can provide the physical and topological data required to
describe the architecture of the network and to build physical models. The new
algorithm can be used for automated phenotypic characterization of any weighted
network whose structure is dominated by cycles...

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## Spectral analysis of deformed random networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.62%

We study spectral behavior of sparsely connected random networks under the
random matrix framework. Sub-networks without any connection among them form a
network having perfect community structure. As connections among the
sub-networks are introduced, the spacing distribution shows a transition from
the Poisson statistics to the Gaussian orthogonal ensemble statistics of random
matrix theory. The eigenvalue density distribution shows a transition to the
Wigner's semicircular behavior for a completely deformed network. The range for
which spectral rigidity, measured by the Dyson-Mehta $\Delta_3$ statistics,
follows the Gaussian orthogonal ensemble statistics depends upon the
deformation of the network from the perfect community structure. The spacing
distribution is particularly useful to track very slight deformations of the
network from a perfect community structure, whereas the density distribution
and the $\Delta_3$ statistics remain identical to the undeformed network. On
the other hand the $\Delta_3$ statistics is useful for the larger deformation
strengths. Finally, we analyze the spectrum of a protein-protein interaction
network for Helicobacter, and compare the spectral behavior with those of the
model networks.; Comment: accepted for publication in Phys. Rev. E (replaced with the final
version)

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## Hierarchical ordering of reticular networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 06/10/2011
Português

Relevância na Pesquisa

45.62%

The structure of hierarchical networks in biological and physical systems has
long been characterized using the Horton-Strahler ordering scheme. The scheme
assigns an integer order to each edge in the network based on the topology of
branching such that the order increases from distal parts of the network (e.g.,
mountain streams or capillaries) to the "root" of the network (e.g., the river
outlet or the aorta). However, Horton-Strahler ordering cannot be applied to
networks with loops because they they create a contradiction in the edge
ordering in terms of which edge precedes another in the hierarchy. Here, we
present a generalization of the Horton-Strahler order to weighted planar
reticular networks, where weights are assumed to correlate with the importance
of network edges, e.g., weights estimated from edge widths may correlate to
flow capacity. Our method assigns hierarchical levels not only to edges of the
network, but also to its loops, and classifies the edges into reticular edges,
which are responsible for loop formation, and tree edges. In addition, we
perform a detailed and rigorous theoretical analysis of the sensitivity of the
hierarchical levels to weight perturbations. We discuss applications of this
generalized Horton-Strahler ordering to the study of leaf venation and other
biological networks.; Comment: 9 pages...

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## Analysis of the structure of complex networks at different resolution levels

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.62%

#Physics - Data Analysis, Statistics and Probability#Condensed Matter - Other Condensed Matter#Computer Science - Discrete Mathematics#Physics - Physics and Society#Quantitative Biology - Quantitative Methods

Modular structure is ubiquitous in real-world complex networks, and its
detection is important because it gives insights in the structure-functionality
Modular structure is ubiquitous in real-world complex networks, and its
detection is important because it gives insights in the structure-functionality
relationship. The standard approach is based on the optimization of a quality
function, modularity, which is a relative quality measure for a partition of a
network into modules. Recently some authors [1,2] have pointed out that the
optimization of modularity has a fundamental drawback: the existence of a
resolution limit beyond which no modular structure can be detected even though
these modules might have own entity. The reason is that several topological
descriptions of the network coexist at different scales, which is, in general,
a fingerprint of complex systems. Here we propose a method that allows for
multiple resolution screening of the modular structure. The method has been
validated using synthetic networks, discovering the predefined structures at
all scales. Its application to two real social networks allows to find the
exact splits reported in the literature, as well as the substructure beyond the
actual split.; Comment: 23 pages...

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## Fast Response to Infection Spread and Cyber Attacks on Large-Scale Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 30/06/2012
Português

Relevância na Pesquisa

45.62%

#Computer Science - Social and Information Networks#Computer Science - Cryptography and Security#Physics - Physics and Society#Quantitative Biology - Quantitative Methods

We present a strategy for designing fast methods of response to cyber attacks
and infection spread on complex weighted networks. In these networks, nodes can
be interpreted as primitive elements of the system, and weighted edges reflect
the strength of interaction among these elements. The proposed strategy belongs
to the family of multiscale methods whose goal is to approximate the system at
multiple scales of coarseness and to obtain a solution of microscopic scale by
combining the information from coarse scales. In recent years these methods
have demonstrated their potential for solving optimization and analysis
problems on large-scale networks. We consider an optimization problem that is
based on the SIS epidemiological model. The objective is to detect the network
nodes that have to be immunized in order to keep a low level of infection in
the system.

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## Emergence of Complex Dynamics in a Simple Model of Signaling Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 19/11/2004
Português

Relevância na Pesquisa

45.62%

#Quantitative Biology - Other Quantitative Biology#Condensed Matter - Soft Condensed Matter#Physics - Biological Physics

A variety of physical, social and biological systems generate complex
fluctuations with correlations across multiple time scales. In physiologic
systems, these long-range correlations are altered with disease and aging. Such
correlated fluctuations in living systems have been attributed to the
interaction of multiple control systems; however, the mechanisms underlying
this behavior remain unknown. Here, we show that a number of distinct classes
of dynamical behaviors, including correlated fluctuations characterized by
$1/f$-scaling of their power spectra, can emerge in networks of simple
signaling units. We find that under general conditions, complex dynamics can be
generated by systems fulfilling two requirements: i) a ``small-world'' topology
and ii) the presence of noise. Our findings support two notable conclusions:
first, complex physiologic-like signals can be modeled with a minimal set of
components; and second, systems fulfilling conditions (i) and (ii) are robust
to some degree of degradation, i.e., they will still be able to generate
$1/f$-dynamics.

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## Thresholds, long delays and stability from generalized allosteric effect in protein networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 26/01/2006
Português

Relevância na Pesquisa

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Post-transductional modifications tune the functions of proteins and regulate
the collective dynamics of biochemical networks that determine how cells
respond to environmental signals. For example, protein phosphorylation and
nitrosylation are well-known to play a pivotal role in the intracellular
transduction of activation and death signals. A protein can have multiple sites
where chemical groups can reversibly attach in processes such as
phosphorylation or nitrosylation. A microscopic description of these processes
must take into account the intrinsic probabilistic nature of the underlying
reactions. We apply combinatorial considerations to standard enzyme kinetics
and in this way we extend to the dynamic regime a simplified version of the
traditional models on the allosteric regulation of protein functions. We link a
generic modification chain to a downstream Michaelis-Menten enzymatic reaction
and we demonstrate numerically that this accounts both for thresholds and long
time delays in the conversion of the substrate by the enzyme. The proposed
mechanism is stable and robust and the higher the number of modification sites,
the greater the stability. We show that a high number of modification sites
converts a fast reaction into a slow process...

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## Neutral networks of genotypes: Evolution behind the curtain

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 13/02/2010
Português

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Our understanding of the evolutionary process has gone a long way since the
publication, 150 years ago, of "On the origin of species" by Charles R. Darwin.
The XXth Century witnessed great efforts to embrace replication, mutation, and
selection within the framework of a formal theory, able eventually to predict
the dynamics and fate of evolving populations. However, a large body of
empirical evidence collected over the last decades strongly suggests that some
of the assumptions of those classical models necessitate a deep revision. The
viability of organisms is not dependent on a unique and optimal genotype. The
discovery of huge sets of genotypes (or neutral networks) yielding the same
phenotype --in the last term the same organism--, reveals that, most likely,
very different functional solutions can be found, accessed and fixed in a
population through a low-cost exploration of the space of genomes. The
'evolution behind the curtain' may be the answer to some of the current puzzles
that evolutionary theory faces, like the fast speciation process that is
observed in the fossil record after very long stasis periods.; Comment: 7 pages, 7 color figures, uses a modification of pnastwo.cls called
pnastwo-modified.cls (included)

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## Calcium and synaptic dynamics underlying reverberatory activity in neuronal networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/06/2007
Português

Relevância na Pesquisa

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Persistent activity is postulated to drive neural network plasticity and
learning. To investigate its underlying cellular mechanisms, we developed a
biophysically tractable model that explains the emergence, sustenance, and
eventual termination of short-term persistent activity. Using the model, we
reproduced the features of reverberating activity that were observed in small
(50-100 cells) networks of cultured hippocampal neurons, such as the appearance
of polysynaptic current clusters, the typical inter-cluster intervals, the
typical duration of reverberation, and the response to changes in
extra-cellular ionic composition. The model relies on action
potential-triggered residual presynaptic calcium, which we suggest plays an
important role in sustaining reverberations. We show that reverberatory
activity is maintained by enhanced asynchronous transmitter release from
pre-synaptic terminals, which in itself depends on the dynamics of residual
presynaptic calcium. Hence, asynchronous release, rather than being a "synaptic
noise", can play an important role in network dynamics. Additionally, we found
that a fast timescale synaptic depression is responsible for oscillatory
network activation during reverberations, whereas the onset of a slow timescale
depression leads to the termination of reverberation. The simplicity of our
model enabled a number of predictions that were confirmed by additional
analyses of experimental manipulations.

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## Vascular networks due to dynamically arrested crystalline ordering of elongated cells

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.62%

#Quantitative Biology - Cell Behavior#Condensed Matter - Soft Condensed Matter#Physics - Biological Physics#Quantitative Biology - Tissues and Organs#92C15, 92C17#J.3#I.6.8

Recent experimental and theoretical studies suggest that crystallization and
glass-like solidification are useful analogies for understanding cell ordering
in confluent biological tissues. It remains unexplored how cellular ordering
contributes to pattern formation during morphogenesis. With a computational
model we show that a system of elongated, cohering biological cells can get
dynamically arrested in a network pattern. Our model provides a new explanation
for the formation of cellular networks in culture systems that exclude
intercellular interaction via chemotaxis or mechanical traction.; Comment: 11 pages, 4 figures. Published as: Palm and Merks (2013) Physical
Review E 87, 012725. The present version includes a correction in the
calculation of the nematic order parameter. Erratum submitted to PRE on Jun
5th 2013. The correction does not affect the conclusions

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## Annealed and Mean-Field formulations of Disease Dynamics on Static and Adaptive Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.62%

#Physics - Physics and Society#Condensed Matter - Statistical Mechanics#Physics - Computational Physics#Quantitative Biology - Other Quantitative Biology

We use the annealed formulation of complex networks to study the dynamical
behavior of disease spreading on both static and adaptive networked systems.
This unifying approach relies on the annealed adjacency matrix, representing
one network ensemble, and allows to solve the dynamical evolution of the whole
network ensemble all at once. Our results accurately reproduce those obtained
by extensive numerical simulations showing a large improvement with respect to
the usual heterogeneous mean-field formulation. Moreover, by means of the
annealed formulation we derive a new heterogeneous mean-field formulation that
correctly reproduces the epidemic dynamics.; Comment: 5 pages, 3 Figures. Final version published in Physical Review E
(Rapid Comm.)

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## Parameter identification in large kinetic networks with BioPARKIN

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.62%

#Computer Science - Mathematical Software#Computer Science - Computational Engineering, Finance, and Science#Quantitative Biology - Quantitative Methods#65L09 (Primary) 49M15, 65C20, 65L04, 65L80, 92C42 (Secondary)#G.1.6#G.1.7#J.3

Modelling, parameter identification, and simulation play an important role in
systems biology. Usually, the goal is to determine parameter values that
minimise the difference between experimental measurement values and model
predictions in a least-squares sense. Large-scale biological networks, however,
often suffer from missing data for parameter identification. Thus, the
least-squares problems are rank-deficient and solutions are not unique. Many
common optimisation methods ignore this detail because they do not take into
account the structure of the underlying inverse problem. These algorithms
simply return a "solution" without additional information on identifiability or
uniqueness. This can yield misleading results, especially if parameters are
co-regulated and data are noisy.; Comment: 20 pages, 7 figures, 4 tables; added 1 figure, and revised section 4

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## Disease Spreading in Structured Scale-Free Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 17/10/2002
Português

Relevância na Pesquisa

45.62%

#Condensed Matter - Statistical Mechanics#Condensed Matter - Other Condensed Matter#Quantitative Biology - Other Quantitative Biology

We study the spreading of a disease on top of structured scale-free networks
recently introduced. By means of numerical simulations we analyze the SIS and
the SIR models. Our results show that when the connectivity fluctuations of the
network are unbounded whether the epidemic threshold exists strongly depends on
the initial density of infected individuals and the type of epidemiological
model considered. Analytical arguments are provided in order to account for the
observed behavior. We conclude that the peculiar topological features of this
network and the absence of small-world properties determine the dynamics of
epidemic spreading.; Comment: 7 pages, 6 figures. EPJ style

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## On Projection-Based Model Reduction of Biochemical Networks-- Part II: The Stochastic Case

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 24/03/2014
Português

Relevância na Pesquisa

45.62%

#Mathematics - Optimization and Control#Computer Science - Systems and Control#Quantitative Biology - Quantitative Methods

In this paper, we consider the problem of model order reduction of stochastic
biochemical networks. In particular, we reduce the order of (the number of
equations in) the Linear Noise Approximation of the Chemical Master Equation,
which is often used to describe biochemical networks. In contrast to other
biochemical network reduction methods, the presented one is projection-based.
Projection-based methods are powerful tools, but the cost of their use is the
loss of physical interpretation of the nodes in the network. In order alleviate
this drawback, we employ structured projectors, which means that some nodes in
the network will keep their physical interpretation. For many models in
engineering, finding structured projectors is not always feasible; however, in
the context of biochemical networks it is much more likely as the networks are
often (almost) monotonic. To summarise, the method can serve as a trade-off
between approximation quality and physical interpretation, which is illustrated
on numerical examples.; Comment: Submitted to the 53rd CDC

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## Dynamic behaviors in directed networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 11/09/2006
Português

Relevância na Pesquisa

45.62%

Motivated by the abundance of directed synaptic couplings in a real
biological neuronal network, we investigate the synchronization behavior of the
Hodgkin-Huxley model in a directed network. We start from the standard model of
the Watts-Strogatz undirected network and then change undirected edges to
directed arcs with a given probability, still preserving the connectivity of
the network. A generalized clustering coefficient for directed networks is
defined and used to investigate the interplay between the synchronization
behavior and underlying structural properties of directed networks. We observe
that the directedness of complex networks plays an important role in emerging
dynamical behaviors, which is also confirmed by a numerical study of the
sociological game theoretic voter model on directed networks.

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