5,548 research outputs found
Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network
Abstract
Background
Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses.
Results
We applied our novel approach to address the Predictive Signaling Network Modeling challenge of the fourth (2009) Dialog for Reverse Engineering Assessment's and Methods (DREAM4) competition. The challenge results showed that our method accurately captured signal transduction of a network of protein kinases and phosphoproteins in that the predicted protein phosphorylation levels under all experimental conditions were highly correlated (R2 = 0.93) with the observed results. Based on the evaluation of the DREAM4 organizer, our team was ranked as one of the top five best performers in predicting network structure and protein phosphorylation activity under test conditions.
Conclusions
Bayesian network can be used to simulate the propagation of signals in cellular systems. Incorporating the Ontology Fingerprint as prior biological knowledge allows us to efficiently infer concise signaling network structure and to accurately predict cellular responses.http://deepblue.lib.umich.edu/bitstream/2027.42/109490/1/12918_2012_Article_989.pd
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Modeling the Heart as a Communication System
Electrical communication between cardiomyocytes can be perturbed during
arrhythmia, but these perturbations are not captured by conventional
electrocardiographic metrics. We developed a theoretical framework to quantify
electrical communication using information theory metrics in 2-dimensional cell
lattice models of cardiac excitation propagation. The time series generated by
each cell was coarse-grained to 1 when excited or 0 when resting. The Shannon
entropy for each cell was calculated from the time series during four
clinically important heart rhythms: normal heartbeat, anatomical reentry,
spiral reentry, and multiple reentry. We also used mutual information to
perform spatial profiling of communication during these cardiac arrhythmias. We
found that information sharing between cells was spatially heterogeneous. In
addition, cardiac arrhythmia significantly impacted information sharing within
the heart. Entropy localized the path of the drifting core of spiral reentry,
which could be an optimal target of therapeutic ablation. We conclude that
information theory metrics can quantitatively assess electrical communication
among cardiomyocytes. The traditional concept of the heart as a functional
syncytium sharing electrical information cannot predict altered entropy and
information sharing during complex arrhythmia. Information theory metrics may
find clinical application in the identification of rhythm-specific treatments
which are currently unmet by traditional electrocardiographic techniques.Comment: 26 pages (including Appendix), 6 figures, 8 videos (not uploaded due
to size limitation
Wireless Network Stability in the SINR Model
We study the stability of wireless networks under stochastic arrival
processes of packets, and design efficient, distributed algorithms that achieve
stability in the SINR (Signal to Interference and Noise Ratio) interference
model.
Specifically, we make the following contributions. We give a distributed
algorithm that achieves -efficiency on all networks
(where is the number of links in the network), for all length monotone,
sub-linear power assignments. For the power control version of the problem, we
give a distributed algorithm with -efficiency (where is the length diversity of the link set).Comment: 10 pages, appeared in SIROCCO'1
Role of relaxation time scale in noisy signal transduction
Intracellular fluctuations, mainly triggered by gene expression, are an
inevitable phenomenon observed in living cells. It influences generation of
phenotypic diversity in genetically identical cells. Such variation of cellular
components is beneficial in some contexts but detrimental in others. To
quantify the fluctuations in a gene product, we undertake an analytical scheme
for studying few naturally abundant linear as well as branched chain network
motifs. We solve the Langevin equations associated with each motif under the
purview of linear noise approximation and quantify Fano factor and mutual
information. Both quantifiable expressions exclusively depend on the relaxation
time (decay rate constant) and steady state population of the network
components. We investigate the effect of relaxation time constraints on Fano
factor and mutual information to indentify a time scale domain where a network
can recognize the fluctuations associated with the input signal more reliably.
We also show how input population affects both quantities. We extend our
calculation to long chain linear motif and show that with increasing chain
length, the Fano factor value increases but the mutual information processing
capability decreases. In this type of motif, the intermediate components are
shown to act as a noise filter that tune up input fluctuations and maintain
optimum fluctuations in the output. For branched chain motifs, both quantities
vary within a large scale due to their network architecture and facilitate
survival of living system in diverse environmental conditions.Comment: 14 pages, 6 figure
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