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## Dinâmica da Fermentação Alcóolica: Aplicação de Redes Booleanas na Dinâmica de Expressão Gênica em Linhagens de Saccharomyces Cerevisiae durante o Processo Fermentativo; Dynamics of alcoholic fermentation: application of Boolean networks in the dynamics of gene expression in Saccharomyces cerevisiae strains during fermentation process

Fonte: Biblioteca Digitais de Teses e Dissertações da USP
Publicador: Biblioteca Digitais de Teses e Dissertações da USP

Tipo: Dissertação de Mestrado
Formato: application/pdf

Publicado em 17/10/2012
PT

Relevância na Pesquisa

45.89%

#bioetanol#bioethanol#genes regulatory networks Boolean Network.#levedura#linhagem PE-2#modelo booleano.#PE-2 strain#redes de regulação gênica#yeast

Na busca por soluções que maximizem a produção de etanol, o melhoramento genético de diferentes linhagens de levedura tornou-se foco de investigação em diversos centros de pesquisa. Com o recente sequenciamento de uma linhagem selvagem utilizada nas usinas sucroalcooleiras brasileiras, a linhagem PE-2 da espécie Saccharomyces cerevisiae, surgiu o interesse em estudar sua dinâmica durante o processo de fermentação a fim de encontrar aspectos que possam explicar como estas se tornaram mais adaptadas às dornas de fermentação mantendo a alta produtividade de bioetanol. A partir da análise transcricional da linhagem PE-2, Buscamos por métodos de inferência de redes que possam representar a dinâmica dessa levedura. Propomos nesse trabalho a modelagem de dados experimentais temporais das linhagens PE-2 e S288c (utilizada como referência) baseado em um modelo de Redes Booleanas. Trata-se de um modelo onde convertemos dados contínuos em dados discretos (0 or 1) no qual, de acordo com restrições ditadas pelo modelo, são inferidas redes que representem interações gênicas ao longo do tempo baseados nas amostras temporais. Conseguimos modelar, com sucesso, algumas redes utilizando conjuntos com 11 e 12 genes relacionados a genes pertencentes à via da glicólise e fermentação da levedura.; Ethanol production improvements give rise to the breeding of yeast strains...

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## Shape and efficiency of wood ant foraging networks

Fonte: Springer Verlag
Publicador: Springer Verlag

Tipo: Artigo de Revista Científica

Publicado em //2009
EN

Relevância na Pesquisa

45.77%

We measured the shape of the foraging trail networks of 11 colonies of the wood ant Formica aquilonia (Formica rufa group). We characterized these networks in terms of their degree of branching and the angles between branches, as well as in terms of their efficiency. The measured networks were compared with idealized model networks built to optimize one of two components of efficiency, total length (i.e., total amount of trail) and route factor (i.e., average distance between nest and foraging site). The analysis shows that the networks built by the ants obtain a compromise between the two modes of efficiency. These results are largely independent of the size of the network or colony size. The ants’ efficiency is comparable to that of networks built by humans but achieved without the benefit of centralized control.; Jerome Buhl, Kerri Hicks, Esther R. Miller, Sophie Persey, Ola Alinvi, David J. T. Sumpter

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## Exact Controllability of Complex Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 22/10/2013

Relevância na Pesquisa

45.79%

#Physics - Physics and Society#Condensed Matter - Disordered Systems and Neural Networks#Computer Science - Social and Information Networks

Controlling complex networks is of paramount importance in science and
engineering. Despite the recent development of structural-controllability
theory, we continue to lack a framework to control undirected complex networks,
especially given link weights. Here we introduce an exact-controllability
paradigm based on the maximum multiplicity to identify the minimum set of
driver nodes required to achieve full control of networks with arbitrary
structures and link-weight distributions. The framework reproduces the
structural controllability of directed networks characterized by structural
matrices. We explore the controllability of a large number of real and model
networks, finding that dense networks with identical weights are difficult to
be controlled. An efficient and accurate tool is offered to assess the
controllability of large sparse and dense networks. The exact-controllability
framework enables a comprehensive understanding of the impact of network
properties on controllability, a fundamental problem towards our ultimate
control of complex systems.; Comment: 19 pages, 3 figures, 3 tables

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## Synchronization in model networks of class I neurons

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

45.77%

#Nonlinear Sciences - Adaptation and Self-Organizing Systems#Condensed Matter - Disordered Systems and Neural Networks#Nonlinear Sciences - Pattern Formation and Solitons#Quantitative Biology - Neurons and Cognition

We study a modification of the canonical model for networks of class I
neurons, presented by Hoppensteadt and Izhikevich, in which the 'pulse' emitted
by a neuron is smooth rather than a delta-function. We prove two types of
results about synchronization and desynchronization of such networks, the first
type pertaining to 'pulse' functions which are symmetric, and the other type in
the regime in which each neuron is connected to many other neurons.; Comment: 14 pages. An error has been found in the asymptotic analysis in
section 5 of the first version, due to which some of the claims made in this
section were untrue. In this version the correct results on
synchronization/desynchronization in the 'many connections' regime are given

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## Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 27/05/2006

Relevância na Pesquisa

45.77%

Song, Havlin and Makse (2005) have recently used a version of the
box-counting method, called the node-covering method, to quantify the
self-similar properties of 43 cellular networks: the minimal number $N_V$ of
boxes of size $\ell$ needed to cover all the nodes of a cellular network was
found to scale as the power law $N_V \sim (\ell+1)^{-D_V}$ with a fractal
dimension $D_V=3.53\pm0.26$. We propose a new box-counting method based on
edge-covering, which outperforms the node-covering approach when applied to
strictly self-similar model networks, such as the Sierpinski network. The
minimal number $N_E$ of boxes of size $\ell$ in the edge-covering method is
obtained with the simulated annealing algorithm. We take into account the
possible discrete scale symmetry of networks (artifactual and/or real), which
is visualized in terms of log-periodic oscillations in the dependence of the
logarithm of $N_E$ as a function of the logarithm of $\ell$. In this way, we
are able to remove the bias of the estimator of the fractal dimension, existing
for finite networks. With this new methodology, we find that $N_E$ scales with
respect to $\ell$ as a power law $N_E \sim \ell^{-D_E}$ with $D_E=2.67\pm0.15$
for the 43 cellular networks previously analyzed by Song...

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## Branching process approach for Boolean bipartite networks of metabolic reactions

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

45.76%

#Condensed Matter - Statistical Mechanics#Physics - Biological Physics#Quantitative Biology - Molecular Networks

The branching process (BP) approach has been successful in explaining the
avalanche dynamics in complex networks. However, its applications are mainly
focused on unipartite networks, in which all nodes are of the same type. Here,
motivated by a need to understand avalanche dynamics in metabolic networks, we
extend the BP approach to a particular bipartite network composed of Boolean
AND and OR logic gates. We reduce the bipartite network into a unipartite
network by integrating out OR gates, and obtain the effective branching ratio
for the remaining AND gates. Then the standard BP approach is applied to the
reduced network, and the avalanche size distribution is obtained. We test the
BP results with simulations on the model networks and two microbial metabolic
networks, demonstrating the usefulness of the BP approach.

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## Range-limited Centrality Measures in Complex Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 22/11/2011

Relevância na Pesquisa

45.77%

#Physics - Physics and Society#Condensed Matter - Statistical Mechanics#Computer Science - Data Structures and Algorithms#Computer Science - Social and Information Networks

Here we present a range-limited approach to centrality measures in both
non-weighted and weighted directed complex networks. We introduce an efficient
method that generates for every node and every edge its betweenness centrality
based on shortest paths of lengths not longer than $\ell = 1,...,L$ in case of
non-weighted networks, and for weighted networks the corresponding quantities
based on minimum weight paths with path weights not larger than $w_{\ell}=\ell
\Delta$, $\ell=1,2...,L=R/\Delta$. These measures provide a systematic
description on the positioning importance of a node (edge) with respect to its
network neighborhoods 1-step out, 2-steps out, etc. up to including the whole
network. We show that range-limited centralities obey universal scaling laws
for large non-weighted networks. As the computation of traditional centrality
measures is costly, this scaling behavior can be exploited to efficiently
estimate centralities of nodes and edges for all ranges, including the
traditional ones. The scaling behavior can also be exploited to show that the
ranking top-list of nodes (edges) based on their range-limited centralities
quickly freezes as function of the range, and hence the diameter-range top-list
can be efficiently predicted. We also show how to estimate the typical largest
node-to-node distance for a network of $N$ nodes...

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## Finding communities in sparse networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 22/09/2015

Relevância na Pesquisa

45.8%

Spectral algorithms based on matrix representations of networks are often
used to detect communities but classic spectral methods based on the adjacency
matrix and its variants fail to detect communities in sparse networks. New
spectral methods based on non-backtracking random walks have recently been
introduced that successfully detect communities in many sparse networks.
However, the spectrum of non-backtracking random walks ignores hanging trees in
networks that can contain information about the community structure of
networks. We introduce the reluctant backtracking operators that explicitly
account for hanging trees as they admit a small probability of returning to the
immediately previous node unlike the non-backtracking operators that forbid an
immediate return. We show that the reluctant backtracking operators can detect
communities in certain sparse networks where the non-backtracking operators
cannot while performing comparably on benchmark stochastic block model networks
and real world networks. We also show that the spectrum of the reluctant
backtracking operator approximately optimises the standard modularity function
similar to the flow matrix. Interestingly, for this family of non- and
reluctant-backtracking operators the main determinant of performance on
real-world networks is whether or not they are normalised to conserve
probability at each node.; Comment: 11 pages...

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## Robustness of Transcriptional Regulation in Yeast-like Model Boolean Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

45.88%

We investigate the dynamical properties of the transcriptional regulation of
gene expression in the yeast Saccharomyces Cerevisiae within the framework of a
synchronously and deterministically updated Boolean network model. By means of
a dynamically determinant subnetwork, we explore the robustness of
transcriptional regulation as a function of the type of Boolean functions used
in the model that mimic the influence of regulating agents on the transcription
level of a gene. We compare the results obtained for the actual yeast network
with those from two different model networks, one with similar in-degree
distribution as the yeast and random otherwise, and another due to Balcan et
al., where the global topology of the yeast network is reproduced faithfully.
We, surprisingly, find that the first set of model networks better reproduce
the results found with the actual yeast network, even though the Balcan et al.
model networks are structurally more similar to that of yeast.; Comment: 7 pages, 4 figures, To appear in Int. J. Bifurcation and Chaos, typos
were corrected and 2 references were added

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## Spectral Properties of Directed Random Networks with Modular Structure

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

45.85%

#Condensed Matter - Disordered Systems and Neural Networks#Computer Science - Social and Information Networks#Physics - Physics and Society#Quantitative Biology - Molecular Networks

We study spectra of directed networks with inhibitory and excitatory
couplings. We investigate in particular eigenvector localization properties of
various model networks for different value of correlation among their entries.
Spectra of random networks, with completely uncorrelated entries show a
circular distribution with delocalized eigenvectors, where as networks with
correlated entries have localized eigenvectors. In order to understand the
origin of localization we track the spectra as a function of connection
probability and directionality. As connections are made directed, eigenstates
start occurring in complex conjugate pairs and the eigenvalue distribution
combined with the localization measure shows a rich pattern. Moreover, for a
very well distinguished community structure, the whole spectrum is localized
except few eigenstates at boundary of the circular distribution. As the network
deviates from the community structure there is a sudden change in the
localization property for a very small value of deformation from the perfect
community structure. We search for this effect for the whole range of
correlation strengths and for different community configurations. Furthermore,
we investigate spectral properties of a metabolic network of zebrafish...

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## Deciphering the global organization of clustering in real complex networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/06/2013

Relevância na Pesquisa

45.88%

#Physics - Physics and Society#Condensed Matter - Disordered Systems and Neural Networks#Computer Science - Social and Information Networks

We uncover the global organization of clustering in real complex networks. As
it happens with other fundamental properties of networks such as the degree
distribution, we find that real networks are neither completely random nor
ordered with respect to clustering, although they tend to be closer to
maximally random architectures. We reach this conclusion by comparing the
global structure of clustering in real networks with that in maximally random
and in maximally ordered clustered graphs. The former are produced with an
exponential random graph model that maintains correlations among adjacent edges
at the minimum needed to conform with the expected clustering spectrum; the
later with a random model that arranges triangles in cliques inducing highly
ordered structures. To compare the global organization of clustering in real
and model networks, we compute $m$-core landscapes, where the $m$-core is
defined, akin to the $k$-core, as the maximal subgraph with edges participating
at least in $m$ triangles. This property defines a set of nested subgraphs
that, contrarily to $k$-cores, is able to distinguish between hierarchical and
modular architectures. To visualize the $m$-core decomposition we developed the
LaNet-vi 3.0 tool.

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## Classification of weighted networks through mesoscale homological features

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 20/12/2015

Relevância na Pesquisa

45.85%

#Mathematics - Combinatorics#Computer Science - Discrete Mathematics#Computer Science - Social and Information Networks#55U10

As complex networks find applications in a growing range of disciplines, the
diversity of naturally occurring and model networks being studied is exploding.
The adoption of a well-developed collection of network taxonomies is a natural
method for both organizing this data and understanding deeper relationships
between networks. Most existing metrics for network structure rely on classical
graph-theoretic measures, extracting characteristics primarily related to
individual vertices or paths between them, and thus classify networks from the
perspective of local features. Here, we describe an alternative approach to
studying structure in networks that relies on an algebraic-topological metric
called persistent homology, which studies intrinsically mesoscale structures
called cycles, constructed from cliques in the network. We present a
classification of 14 commonly studied weighted network models into four groups
or classes, and discuss the structural themes arising in each class. Finally,
we compute the persistent homology of two real-world networks and one network
constructed by a common dynamical systems model, and we compare the results
with the three classes to obtain a better understanding of those networks; Comment: 32 pages, 16 figures

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## Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 04/11/2006

Relevância na Pesquisa

45.9%

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

We propose a growing network model that consists of two tunable mechanisms:
growth by merging modules which are represented as complete graphs and a
fitness-driven preferential attachment. Our model exhibits the three prominent
statistical properties are widely shared in real biological networks, for
example gene regulatory, protein-protein interaction, and metabolic networks.
They retain three power law relationships, such as the power laws of degree
distribution, clustering spectrum, and degree-degree correlation corresponding
to scale-free connectivity, hierarchical modularity, and disassortativity,
respectively. After making comparisons of these properties between model
networks and biological networks, we confirmed that our model has inference
potential for evolutionary processes of biological networks.; Comment: 19 pages, 8 figures

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## The Structure and Dynamics of Gene Regulation Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 14/02/2008

Relevância na Pesquisa

45.89%

The structure and dynamics of a typical biological system are complex due to
strong and inhomogeneous interactions between its constituents. The
investigation of such systems with classical mathematical tools, such as
differential equations for their dynamics, is not always suitable. The graph
theoretical models may serve as a rough but powerful tool in such cases. In
this thesis, I first consider the network modeling for the representation of
the biological systems. Both the topological and dynamical investigation tools
are developed and applied to the various model networks. In particular, the
attractor features' scaling with system size and distributions are explored for
model networks. Moreover, the theoretical robustness expressions are discussed
and computational studies are done for confirmation. The main biological
research in this thesis is to investigate the transcriptional regulation of
gene expression with synchronously and deterministically updated Boolean
network models. I explore the attractor structure and the robustness of the
known interaction network of the yeast, Saccharomyces Cerevisiae and compare
with the model networks. Furthermore, I discuss a recent model claiming a
possible root to the topology of the yeast's gene regulation network and
investigate this model dynamically. The thesis also included another study
which investigates a relation between folding kinetics with a new network
representation...

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## Origin and implications of zero degeneracy in networks spectra

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

45.77%

#Condensed Matter - Disordered Systems and Neural Networks#Physics - Physics and Society#Quantitative Biology - Molecular Networks

Spectra of real world networks exhibit properties which are different from
the random networks. One such property is the existence of a very high
degeneracy at zero eigenvalues. In this work, we provide possible reasons
behind occurrence of the zero degeneracy in various networks spectra.
Comparison of zero degeneracy in protein-protein interaction networks of six
different species and in their corresponding model networks sheds light in
understanding the evolution of complex biological systems.; Comment: 5 pages, 3 figures

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## Chaotic Gene Regulatory Networks Can Be Robust Against Mutations and Noise

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Relevância na Pesquisa

45.88%

Robustness to mutations and noise has been shown to evolve through
stabilizing selection for optimal phenotypes in model gene regulatory networks.
The ability to evolve robust mutants is known to depend on the network
architecture. How do the dynamical properties and state-space structures of
networks with high and low robustness differ? Does selection operate on the
global dynamical behavior of the networks? What kind of state-space structures
are favored by selection? We provide damage propagation analysis and an
extensive statistical analysis of state spaces of these model networks to show
that the change in their dynamical properties due to stabilizing selection for
optimal phenotypes is minor. Most notably, the networks that are most robust to
both mutations and noise are highly chaotic. Certain properties of chaotic
networks, such as being able to produce large attractor basins, can be useful
for maintaining a stable gene-expression pattern. Our findings indicate that
conventional measures of stability, such as the damage-propagation rate, do not
provide much information about robustness to mutations or noise in model gene
regulatory networks.; Comment: JTB accepted

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## Learning multifractal structure in large networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 26/02/2014

Relevância na Pesquisa

45.85%

Generating random graphs to model networks has a rich history. In this paper,
we analyze and improve upon the multifractal network generator (MFNG)
introduced by Palla et al. We provide a new result on the probability of
subgraphs existing in graphs generated with MFNG. From this result it follows
that we can quickly compute moments of an important set of graph properties,
such as the expected number of edges, stars, and cliques. Specifically, we show
how to compute these moments in time complexity independent of the size of the
graph and the number of recursive levels in the generative model. We leverage
this theory to a new method of moments algorithm for fitting large networks to
MFNG. Empirically, this new approach effectively simulates properties of
several social and information networks. In terms of matching subgraph counts,
our method outperforms similar algorithms used with the Stochastic Kronecker
Graph model. Furthermore, we present a fast approximation algorithm to generate
graph instances following the multi- fractal structure. The approximation
scheme is an improvement over previous methods, which ran in time complexity
quadratic in the number of vertices. Combined, our method of moments and fast
sampling scheme provide the first scalable framework for effectively modeling
large networks with MFNG.

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## Modeling the topology of protein interaction networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 28/07/2011

Relevância na Pesquisa

45.83%

#Physics - Biological Physics#Condensed Matter - Statistical Mechanics#Quantitative Biology - Molecular Networks

A major issue in biology is the understanding of the interactions between
proteins. These interactions can be described by a network, where the proteins
are modeled by nodes and the interactions by edges. The origin of these protein
networks is not well understood yet. Here we present a two-step model, which
generates clusters with the same topological properties as networks for
protein-protein interactions, namely, the same degree distribution, cluster
size distribution, clustering coefficient and shortest path length. The
biological and model networks are not scale free but exhibit small world
features. The model allows the fitting of different biological systems by
tuning a single parameter.; Comment: 5 pages, 5 figures

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## Sparse essential interactions in model networks of gene regulation

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 21/10/2009

Relevância na Pesquisa

45.88%

Gene regulatory networks typically have low in-degrees, whereby any given
gene is regulated by few of the genes in the network. What mechanisms might be
responsible for these low in-degrees? Starting with an accepted framework of
the binding of transcription factors to DNA, we consider a simple model of gene
regulatory dynamics. In this model, we show that the constraint of having a
given function leads to the emergence of minimum connectivities compatible with
function. We exhibit mathematically this behavior within a limit of our model
and show that it also arises in the full model. As a consequence, functionality
in these gene networks is parsimonious, i.e., is concentrated on a sparse
number of interactions as measured for instance by their essentiality. Our
model thus provides a simple mechanism for the emergence of sparse regulatory
networks, and leads to very heterogeneous effects of mutations.; Comment: 9 pages, 5 figures

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## Transport of multiple users in complex networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/09/2006

Relevância na Pesquisa

45.86%

We study the transport properties of model networks such as scale-free and
Erd\H{o}s-R\'{e}nyi networks as well as a real network. We consider the
conductance $G$ between two arbitrarily chosen nodes where each link has the
same unit resistance. Our theoretical analysis for scale-free networks predicts
a broad range of values of $G$, with a power-law tail distribution $\Phi_{\rm
SF}(G)\sim G^{-g_G}$, where $g_G=2\lambda -1$, and $\lambda$ is the decay
exponent for the scale-free network degree distribution. We confirm our
predictions by large scale simulations. The power-law tail in $\Phi_{\rm
SF}(G)$ leads to large values of $G$, thereby significantly improving the
transport in scale-free networks, compared to Erd\H{o}s-R\'{e}nyi networks
where the tail of the conductivity distribution decays exponentially. We
develop a simple physical picture of the transport to account for the results.
We study another model for transport, the \emph{max-flow} model, where
conductance is defined as the number of link-independent paths between the two
nodes, and find that a similar picture holds. The effects of distance on the
value of conductance are considered for both models, and some differences
emerge. We then extend our study to the case of multiple sources...

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