55,015 research outputs found
Influence of homology and node-age on the growth of protein-protein interaction networks
Proteins participating in a protein-protein interaction network can be
grouped into homology classes following their common ancestry. Proteins added
to the network correspond to genes added to the classes, so that the dynamics
of the two objects are intrinsically linked. Here, we first introduce a
statistical model describing the joint growth of the network and the
partitioning of nodes into classes, which is studied through a combined
mean-field and simulation approach. We then employ this unified framework to
address the specific issue of the age dependence of protein interactions,
through the definition of three different node wiring/divergence schemes.
Comparison with empirical data indicates that an age-dependent divergence move
is necessary in order to reproduce the basic topological observables together
with the age correlation between interacting nodes visible in empirical data.
We also discuss the possibility of nontrivial joint partition/topology
observables.Comment: 14 pages, 7 figures [accepted for publication in PRE
Analytical results for stochastically growing networks: connection to the zero range process
We introduce a stochastic model of growing networks where both, the number of
new nodes which joins the network and the number of connections, vary
stochastically. We provide an exact mapping between this model and zero range
process, and use this mapping to derive an analytical solution of degree
distribution for any given evolution rule. One can also use this mapping to
infer about a possible evolution rule for a given network. We demonstrate this
for protein-protein interaction (PPI) network for Saccharomyces Cerevisiae.Comment: 4+ pages, revtex, 3 eps figure
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks
One of the key characteristics of cancer cells is an increased phenotypic
plasticity, driven by underlying genetic and epigenetic perturbations. However,
at a systems-level it is unclear how these perturbations give rise to the
observed increased plasticity. Elucidating such systems-level principles is key
for an improved understanding of cancer. Recently, it has been shown that
signaling entropy, an overall measure of signaling pathway promiscuity, and
computable from integrating a sample's gene expression profile with a protein
interaction network, correlates with phenotypic plasticity and is increased in
cancer compared to normal tissue. Here we develop a computational framework for
studying the effects of network perturbations on signaling entropy. We
demonstrate that the increased signaling entropy of cancer is driven by two
factors: (i) the scale-free (or near scale-free) topology of the interaction
network, and (ii) a subtle positive correlation between differential gene
expression and node connectivity. Indeed, we show that if protein interaction
networks were random graphs, described by Poisson degree distributions, that
cancer would generally not exhibit an increased signaling entropy. In summary,
this work exposes a deep connection between cancer, signaling entropy and
interaction network topology.Comment: 20 pages, 5 figures. In Press in Sci Rep 201
Two universal physical principles shape the power-law statistics of real-world networks
The study of complex networks has pursued an understanding of macroscopic
behavior by focusing on power-laws in microscopic observables. Here, we uncover
two universal fundamental physical principles that are at the basis of complex
networks generation. These principles together predict the generic emergence of
deviations from ideal power laws, which were previously discussed away by
reference to the thermodynamic limit. Our approach proposes a paradigm shift in
the physics of complex networks, toward the use of power-law deviations to
infer meso-scale structure from macroscopic observations.Comment: 14 pages, 7 figure
Dynamical and spectral properties of complex networks
Dynamical properties of complex networks are related to the spectral
properties of the Laplacian matrix that describes the pattern of connectivity
of the network. In particular we compute the synchronization time for different
types of networks and different dynamics. We show that the main dependence of
the synchronization time is on the smallest nonzero eigenvalue of the Laplacian
matrix, in contrast to other proposals in terms of the spectrum of the
adjacency matrix. Then, this topological property becomes the most relevant for
the dynamics.Comment: 14 pages, 5 figures, to be published in New Journal of Physic
Are there laws of genome evolution?
Research in quantitative evolutionary genomics and systems biology led to the
discovery of several universal regularities connecting genomic and molecular
phenomic variables. These universals include the log-normal distribution of the
evolutionary rates of orthologous genes; the power law-like distributions of
paralogous family size and node degree in various biological networks; the
negative correlation between a gene's sequence evolution rate and expression
level; and differential scaling of functional classes of genes with genome
size. The universals of genome evolution can be accounted for by simple
mathematical models similar to those used in statistical physics, such as the
birth-death-innovation model. These models do not explicitly incorporate
selection, therefore the observed universal regularities do not appear to be
shaped by selection but rather are emergent properties of gene ensembles.
Although a complete physical theory of evolutionary biology is inconceivable,
the universals of genome evolution might qualify as 'laws of evolutionary
genomics' in the same sense 'law' is understood in modern physics.Comment: 17 pages, 2 figure
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