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## Epidemic variability in complex networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

#Condensed Matter - Statistical Mechanics#Condensed Matter - Other Condensed Matter#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Other Quantitative Biology

We study numerically the variability of the outbreak of diseases on complex
networks. We use a SI model to simulate the disease spreading at short times,
in homogeneous and in scale-free networks. In both cases, we study the effect
of initial conditions on the epidemic's dynamics and its variability. The
results display a time regime during which the prevalence exhibits a large
sensitivity to noise. We also investigate the dependence of the infection time
on nodes' degree and distance to the seed. In particular, we show that the
infection time of hubs have large fluctuations which limit their reliability as
early-detection stations. Finally, we discuss the effect of the multiplicity of
shortest paths between two nodes on the infection time. Furthermore, we
demonstrate that the existence of even longer paths reduces the average
infection time. These different results could be of use for the design of
time-dependent containment strategies.

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## Discovering universal statistical laws of complex networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 15/12/2011
Português

Relevância na Pesquisa

45.66%

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

Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks...

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## Efficiently inferring community structure in bipartite networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

#Computer Science - Social and Information Networks#Physics - Data Analysis, Statistics and Probability#Physics - Physics and Society#Quantitative Biology - Quantitative Methods#Statistics - Machine Learning

Bipartite networks are a common type of network data in which there are two
types of vertices, and only vertices of different types can be connected. While
bipartite networks exhibit community structure like their unipartite
counterparts, existing approaches to bipartite community detection have
drawbacks, including implicit parameter choices, loss of information through
one-mode projections, and lack of interpretability. Here we solve the community
detection problem for bipartite networks by formulating a bipartite stochastic
block model, which explicitly includes vertex type information and may be
trivially extended to $k$-partite networks. This bipartite stochastic block
model yields a projection-free and statistically principled method for
community detection that makes clear assumptions and parameter choices and
yields interpretable results. We demonstrate this model's ability to
efficiently and accurately find community structure in synthetic bipartite
networks with known structure and in real-world bipartite networks with unknown
structure, and we characterize its performance in practical contexts.; Comment: 12 pages, 9 figures

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## Dreams, endocannabinoids and itinerant dynamics in neural networks: re elaborating Crick-Mitchison unlearning hypothesis

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

#Condensed Matter - Disordered Systems and Neural Networks#Nonlinear Sciences - Chaotic Dynamics#Physics - Popular Physics#Quantitative Biology#Quantitative Biology - Neurons and Cognition

In this work we reevaluate and elaborate Crick-Mitchison's proposal that
REM-sleep corresponds to a self-organized process for unlearning attractors in
neural networks. This reformulation is made at the face of recent findings
concerning the intense activation of the amygdalar complex during REM-sleep,
the role of endocannabinoids in synaptic weakening and neural network models
with itinerant associative dynamics. We distinguish between a neurological
REM-sleep function and a related evolutionary/behavioral dreaming function. At
the neurological level, we propose that REM-sleep regulates excessive
plasticity and weakens over stable brain activation patterns, specially in the
amygdala, hippocampus and motor systems. At the behavioral level, we propose
that dream narrative evolved as exploratory behavior made in a virtual
environment promoting "emotional (un)learning", that is, habituation of
emotional responses, anxiety and fear. We make several experimental predictions
at variance with those of Memory Consolidation Hipothesis. We also predict that
the "replay" of cells ensembles is done at an increasing faster pace along
REM-sleep.; Comment: 18 pages, 2 figures, Revised version (2010)

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## What you see is not what you get: how sampling affects macroscopic features of biological networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/06/2011
Português

Relevância na Pesquisa

45.66%

#Quantitative Biology - Quantitative Methods#Condensed Matter - Disordered Systems and Neural Networks#82B44, 05Cxx

We use mathematical methods from the theory of tailored random graphs to
study systematically the effects of sampling on topological features of large
biological signalling networks. Our aim in doing so is to increase our
quantitative understanding of the relation between true biological networks and
the imperfect and often biased samples of these networks that are reported in
public data repositories and used by biomedical scientists. We derive exact
explicit formulae for degree distributions and degree correlation kernels of
sampled networks, in terms of the degree distributions and degree correlation
kernels of the underlying true network, for a broad family of sampling
protocols that include (un-)biased node and/or link undersampling as well as
(un-)biased link oversampling. Our predictions are in excellent agreement with
numerical simulations.; Comment: 26 pages, 8 figures

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## Reconstruction of biological networks by supervised machine learning approaches

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

We review a recent trend in computational systems biology which aims at using
pattern recognition algorithms to infer the structure of large-scale biological
networks from heterogeneous genomic data. We present several strategies that
have been proposed and that lead to different pattern recognition problems and
algorithms. The strenght of these approaches is illustrated on the
reconstruction of metabolic, protein-protein and regulatory networks of model
organisms. In all cases, state-of-the-art performance is reported.

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## Topology and energy transport in networks of interacting photosynthetic complexes

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

#Physics - Biological Physics#Physics - Chemical Physics#Quantitative Biology - Quantitative Methods

We address the role of topology in the energy transport process that occurs
in networks of photosynthetic complexes. We take inspiration from light
harvesting networks present in purple bacteria and simulate an incoherent
dissipative energy transport process on more general and abstract networks,
considering both regular structures (Cayley trees and hyperbranched fractals)
and randomly-generated ones. We focus on the the two primary light harvesting
complexes of purple bacteria, i.e., the LH1 and LH2, and we use
network-theoretical centrality measures in order to select different LH1
arrangements. We show that different choices cause significant differences in
the transport efficiencies, and that for regular networks centrality measures
allow to identify arrangements that ensure transport efficiencies which are
better than those obtained with a random disposition of the complexes. The
optimal arrangements strongly depend on the dissipative nature of the dynamics
and on the topological properties of the networks considered, and depending on
the latter they are achieved by using global vs. local centrality measures. For
randomly-generated networks a random arrangement of the complexes already
provides efficient transport, and this suggests the process is strong with
respect to limited amount of control in the structure design and to the
disorder inherent in the construction of randomly-assembled structures.
Finally...

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## Inference algorithms for gene networks: a statistical mechanics analysis

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 04/12/2008
Português

Relevância na Pesquisa

45.65%

#Quantitative Biology - Quantitative Methods#Condensed Matter - Disordered Systems and Neural Networks

The inference of gene regulatory networks from high throughput gene
expression data is one of the major challenges in systems biology. This paper
aims at analysing and comparing two different algorithmic approaches. The first
approach uses pairwise correlations between regulated and regulating genes; the
second one uses message-passing techniques for inferring activating and
inhibiting regulatory interactions. The performance of these two algorithms can
be analysed theoretically on well-defined test sets, using tools from the
statistical physics of disordered systems like the replica method. We find that
the second algorithm outperforms the first one since it takes into account
collective effects of multiple regulators.

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## Discriminating different classes of biological networks by analyzing the graphs spectra distribution

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 14/08/2012
Português

Relevância na Pesquisa

45.66%

#Statistics - Methodology#Computer Science - Social and Information Networks#Physics - Physics and Society#Quantitative Biology - Quantitative Methods

The brain's structural and functional systems, protein-protein interaction,
and gene networks are examples of biological systems that share some features
of complex networks, such as highly connected nodes, modularity, and
small-world topology. Recent studies indicate that some pathologies present
topological network alterations relative to norms seen in the general
population. Therefore, methods to discriminate the processes that generate the
different classes of networks (e.g., normal and disease) might be crucial for
the diagnosis, prognosis, and treatment of the disease. It is known that
several topological properties of a network (graph) can be described by the
distribution of the spectrum of its adjacency matrix. Moreover, large networks
generated by the same random process have the same spectrum distribution,
allowing us to use it as a "fingerprint". Based on this relationship, we
introduce and propose the entropy of a graph spectrum to measure the
"uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon
divergences between graph spectra to compare networks. We also introduce
general methods for model selection and network model parameter estimation, as
well as a statistical procedure to test the nullity of divergence between two
classes of complex networks. Finally...

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## Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

#Quantitative Biology - Quantitative Methods#Computer Science - Artificial Intelligence#Computer Science - Computational Engineering, Finance, and Science#Computer Science - Learning

A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to high-throughput phospho-proteomics data was recently introduced and solved
using optimization heuristics based on stochastic methods. Here we demonstrate
how this problem can be solved using Answer Set Programming (ASP), a
declarative problem solving paradigm, in which a problem is encoded as a
logical program such that its answer sets represent solutions to the problem.
ASP has significant improvements over heuristic methods in terms of efficiency
and scalability, it guarantees global optimality of solutions as well as
provides a complete set of solutions. We illustrate the application of ASP with
in silico cases based on realistic networks and data.

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## Dynamics of person-to-person interactions from distributed RFID sensor networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 21/07/2010
Português

Relevância na Pesquisa

45.65%

#Physics - Physics and Society#Condensed Matter - Statistical Mechanics#Computer Science - Human-Computer Interaction#Quantitative Biology - Other Quantitative Biology

Digital networks, mobile devices, and the possibility of mining the
ever-increasing amount of digital traces that we leave behind in our daily
activities are changing the way we can approach the study of human and social
interactions. Large-scale datasets, however, are mostly available for
collective and statistical behaviors, at coarse granularities, while
high-resolution data on person-to-person interactions are generally limited to
relatively small groups of individuals. Here we present a scalable experimental
framework for gathering real-time data resolving face-to-face social
interactions with tunable spatial and temporal granularities. We use active
Radio Frequency Identification (RFID) devices that assess mutual proximity in a
distributed fashion by exchanging low-power radio packets. We analyze the
dynamics of person-to-person interaction networks obtained in three
high-resolution experiments carried out at different orders of magnitude in
community size. The data sets exhibit common statistical properties and lack of
a characteristic time scale from 20 seconds to several hours. The association
between the number of connections and their duration shows an interesting
super-linear behavior, which indicates the possibility of defining
super-connectors both in the number and intensity of connections. Taking
advantage of scalability and resolution...

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## Small-World Propensity in Weighted, Real-World Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 08/05/2015
Português

Relevância na Pesquisa

45.65%

Quantitative descriptions of network structure in big data can provide
fundamental insights into the function of interconnected complex systems.
Small-world structure, commonly diagnosed by high local clustering yet short
average path length between any two nodes, directly enables information flow in
coupled systems, a key function that can differ across conditions or between
groups. However, current techniques to quantify small-world structure are
dependent on nuisance variables such as density and agnostic to critical
variables such as the strengths of connections between nodes, thereby hampering
accurate and comparable assessments of small-world structure in different
networks. Here, we address both limitations with a novel metric called the
Small-World Propensity (SWP). In its binary instantiation, the SWP provides an
unbiased assessment of small-world structure in networks of varying densities.
We extend this concept to the case of weighted networks by developing (i) a
standardized procedure for generating weighted small-world networks, (ii) a
weighted extension of the SWP, and (iii) a stringent and generalizable method
for mapping real-world data onto the theoretical model. In applying these
techniques to real world brain networks...

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## Generic Criticality in Ecological and Neuronal Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 10/08/2015
Português

Relevância na Pesquisa

45.65%

#Condensed Matter - Disordered Systems and Neural Networks#Quantitative Biology - Neurons and Cognition#Quantitative Biology - Populations and Evolution

We investigate the dynamics of two models of biological networks with purely
suppressive interactions between the units; species interacting via niche
competition and neurons via inhibitory synaptic coupling. In both of these
cases, power-law scaling of the density of states with probability arises
without any fine-tuning of the model parameters. These results argue against
the increasingly popular notion that non-equilibrium living systems operate at
special critical points, driven by there by evolution so as to enable adaptive
processing of input data.

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## Epidemic Incidence in Correlated Complex Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 19/09/2003
Português

Relevância na Pesquisa

45.65%

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

We introduce a numerical method to solve epidemic models on the underlying
topology of complex networks. The approach exploits the mean-field like rate
equations describing the system and allows to work with very large system
sizes, where Monte Carlo simulations are useless due to memory needs. We then
study the SIR epidemiological model on assortative networks, providing
numerical evidence of the absence of epidemic thresholds. Besides, the time
profiles of the populations are analyzed. Finally, we stress that the present
method would allow to solve arbitrary epidemic-like models provided that they
can be described by mean-field rate equations.; Comment: 5 pages, 4 figures. Final version published in PRE

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## Time-Ordered Product Expansions for Computational Stochastic Systems Biology

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 24/09/2012
Português

Relevância na Pesquisa

45.65%

#Quantitative Biology - Quantitative Methods#Computer Science - Computational Engineering, Finance, and Science#Nonlinear Sciences - Adaptation and Self-Organizing Systems

The time-ordered product framework of quantum field theory can also be used
to understand salient phenomena in stochastic biochemical networks. It is used
here to derive Gillespie's Stochastic Simulation Algorithm (SSA) for chemical
reaction networks; consequently, the SSA can be interpreted in terms of Feynman
diagrams. It is also used here to derive other, more general simulation and
parameter-learning algorithms including simulation algorithms for networks of
stochastic reaction-like processes operating on parameterized objects, and also
hybrid stochastic reaction/differential equation models in which systems of
ordinary differential equations evolve the parameters of objects that can also
undergo stochastic reactions. Thus, the time-ordered product expansion (TOPE)
can be used systematically to derive simulation and parameter-fitting
algorithms for stochastic systems.; Comment: Submitted to Q-Bio 2012 conference, Santa Fe, New Mexico

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## Maximal Information Transfer and Behavior Diversity in Random Threshold Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/01/2009
Português

Relevância na Pesquisa

45.65%

Random Threshold Networks (RTNs) are an idealized model of diluted, non
symmetric spin glasses, neural networks or gene regulatory networks. RTNs also
serve as an interesting general example of any coordinated causal system. Here
we study the conditions for maximal information transfer and behavior diversity
in RTNs. These conditions are likely to play a major role in physical and
biological systems, perhaps serving as important selective traits in biological
systems. We show that the pairwise mutual information is maximized in
dynamically critical networks. Also, we show that the correlated behavior
diversity is maximized for slightly chaotic networks, close to the critical
region. Importantly, critical networks maximize coordinated, diverse dynamical
behavior across the network and across time: the information transmission
between source and receiver nodes and the diversity of dynamical behaviors,
when measured with a time delay between the source and receiver, are maximized
for critical networks.; Comment: 14 pages, 4 figures

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## The effect of temporal pattern of injury on disability in learning networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 09/05/2012
Português

Relevância na Pesquisa

45.65%

#Quantitative Biology - Neurons and Cognition#Condensed Matter - Disordered Systems and Neural Networks#Quantitative Biology - Tissues and Organs

How networks endure damage is a central issue in neural network research.
This includes temporal as well as spatial pattern of damage. Here, based on
some very simple models we study the difference between a slow-growing and
acute damage and the relation between the size and rate of injury. Our result
shows that in both a three-layer and a homeostasis model a slow-growing damage
has a decreasing effect on network disability as compared with a fast growing
one. This finding is in accord with clinical reports where the state of
patients before and after the operation for slow-growing injuries is much
better that those patients with acute injuries.; Comment: Latex, 17 pages, 7 figures, 2 tables

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## Link communities reveal multiscale complexity in networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.65%

#Physics - Physics and Society#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Quantitative Methods

Networks have become a key approach to understanding systems of interacting
objects, unifying the study of diverse phenomena including biological organisms
and human society. One crucial step when studying the structure and dynamics of
networks is to identify communities: groups of related nodes that correspond to
functional subunits such as protein complexes or social spheres. Communities in
networks often overlap such that nodes simultaneously belong to several groups.
Meanwhile, many networks are known to possess hierarchical organization, where
communities are recursively grouped into a hierarchical structure. However, the
fact that many real networks have communities with pervasive overlap, where
each and every node belongs to more than one group, has the consequence that a
global hierarchy of nodes cannot capture the relationships between overlapping
groups. Here we reinvent communities as groups of links rather than nodes and
show that this unorthodox approach successfully reconciles the antagonistic
organizing principles of overlapping communities and hierarchy. In contrast to
the existing literature, which has entirely focused on grouping nodes, link
communities naturally incorporate overlap while revealing hierarchical
organization. We find relevant link communities in many networks...

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## Maximum Power Efficiency and Criticality in Random Boolean Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.67%

Random Boolean networks are models of disordered causal systems that can
occur in cells and the biosphere. These are open thermodynamic systems
exhibiting a flow of energy that is dissipated at a finite rate. Life does work
to acquire more energy, then uses the available energy it has gained to perform
more work. It is plausible that natural selection has optimized many biological
systems for power efficiency: useful power generated per unit fuel. In this
letter we begin to investigate these questions for random Boolean networks
using Landauer's erasure principle, which defines a minimum entropy cost for
bit erasure. We show that critical Boolean networks maximize available power
efficiency, which requires that the system have a finite displacement from
equilibrium. Our initial results may extend to more realistic models for cells
and ecosystems.; Comment: 4 pages RevTeX, 1 figure in .eps format. Comments welcome, v2: minor
clarifications added, conclusions unchanged. v3: paper rewritten to clarify
it; conclusions unchanged

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## Abrupt structural transitions involving functionally optimal networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 31/08/2005
Português

Relevância na Pesquisa

45.67%

#Physics - Physics and Society#Condensed Matter - Other Condensed Matter#Condensed Matter - Soft Condensed Matter#Quantitative Biology - Other Quantitative Biology

We show analytically that abrupt structural transitions can arise in
functionally optimal networks, driven by small changes in the level of
transport congestion. Our findings are based on an exactly solvable model
system which mimics a variety of biological and social networks. Our results
offer an explanation as to why such diverse sets of network structures arise in
Nature (e.g. fungi) under essentially the same environmental conditions. As a
by-product of this work, we introduce a novel renormalization scheme involving
`cost motifs' which describes analytically the average shortest path across
multiple-ring-and-hub networks.; Comment: 5 pages, 4 figures

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