8,648 research outputs found
Emergence of Blind Areas in Information Spreading
Recently, contagion-based (disease, information, etc.) spreading on social
networks has been extensively studied. In this paper, other than traditional
full interaction, we propose a partial interaction based spreading model,
considering that the informed individuals would transmit information to only a
certain fraction of their neighbors due to the transmission ability in
real-world social networks. Simulation results on three representative networks
(BA, ER, WS) indicate that the spreading efficiency is highly correlated with
the network heterogeneity. In addition, a special phenomenon, namely
\emph{Information Blind Areas} where the network is separated by several
information-unreachable clusters, will emerge from the spreading process.
Furthermore, we also find that the size distribution of such information blind
areas obeys power-law-like distribution, which has very similar exponent with
that of site percolation. Detailed analyses show that the critical value is
decreasing along with the network heterogeneity for the spreading process,
which is complete the contrary to that of random selection. Moreover, the
critical value in the latter process is also larger that of the former for the
same network. Those findings might shed some lights in in-depth understanding
the effect of network properties on information spreading
Recoverable prevalence in growing scale-free networks and the effective immunization
We study the persistent recoverable prevalence and the extinction of computer
viruses via e-mails on a growing scale-free network with new users, which
structure is estimated form real data. The typical phenomenon is simulated in a
realistic model with the probabilistic execution and detection of viruses.
Moreover, the conditions of extinction by random and targeted immunizations for
hubs are derived through bifurcation analysis for simpler models by using a
mean-field approximation without the connectivity correlations. We can
qualitatively understand the mechanisms of the spread in linearly growing
scale-free networks.Comment: 9 pages, 9 figures, 1 table. Update version after helpful referee
comment
Approximation of epidemic models by diffusion processes and their statistical inference
Multidimensional continuous-time Markov jump processes on
form a usual set-up for modeling -like epidemics. However,
when facing incomplete epidemic data, inference based on is not easy
to be achieved. Here, we start building a new framework for the estimation of
key parameters of epidemic models based on statistics of diffusion processes
approximating . First, \previous results on the approximation of
density-dependent -like models by diffusion processes with small diffusion
coefficient , where is the population size, are
generalized to non-autonomous systems. Second, our previous inference results
on discretely observed diffusion processes with small diffusion coefficient are
extended to time-dependent diffusions. Consistent and asymptotically Gaussian
estimates are obtained for a fixed number of observations, which
corresponds to the epidemic context, and for . A
correction term, which yields better estimates non asymptotically, is also
included. Finally, performances and robustness of our estimators with respect
to various parameters such as (the basic reproduction number), ,
are investigated on simulations. Two models, and , corresponding to
single and recurrent outbreaks, respectively, are used to simulate data. The
findings indicate that our estimators have good asymptotic properties and
behave noticeably well for realistic numbers of observations and population
sizes. This study lays the foundations of a generic inference method currently
under extension to incompletely observed epidemic data. Indeed, contrary to the
majority of current inference techniques for partially observed processes,
which necessitates computer intensive simulations, our method being mostly an
analytical approach requires only the classical optimization steps.Comment: 30 pages, 10 figure
The role of multiple marks in epigenetic silencing and the emergence of a stable bivalent chromatin state
We introduce and analyze a minimal model of epigenetic silencing in budding
yeast, built upon known biomolecular interactions in the system. Doing so, we
identify the epigenetic marks essential for the bistability of epigenetic
states. The model explicitly incorporates two key chromatin marks, namely H4K16
acetylation and H3K79 methylation, and explores whether the presence of
multiple marks lead to a qualitatively different systems behavior. We find that
having both modifications is important for the robustness of epigenetic
silencing. Besides the silenced and transcriptionally active fate of chromatin,
our model leads to a novel state with bivalent (i.e., both active and
silencing) marks under certain perturbations (knock-out mutations, inhibition
or enhancement of enzymatic activity). The bivalent state appears under several
perturbations and is shown to result in patchy silencing. We also show that the
titration effect, owing to a limited supply of silencing proteins, can result
in counter-intuitive responses. The design principles of the silencing system
is systematically investigated and disparate experimental observations are
assessed within a single theoretical framework. Specifically, we discuss the
behavior of Sir protein recruitment, spreading and stability of silenced
regions in commonly-studied mutants (e.g., sas2, dot1) illuminating the
controversial role of Dot1 in the systems biology of yeast silencing.Comment: Supplementary Material, 14 page
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