1,048 research outputs found
Mesoscopic organization reveals the constraints governing C. elegans nervous system
One of the biggest challenges in biology is to understand how activity at the
cellular level of neurons, as a result of their mutual interactions, leads to
the observed behavior of an organism responding to a variety of environmental
stimuli. Investigating the intermediate or mesoscopic level of organization in
the nervous system is a vital step towards understanding how the integration of
micro-level dynamics results in macro-level functioning. In this paper, we have
considered the somatic nervous system of the nematode Caenorhabditis elegans,
for which the entire neuronal connectivity diagram is known. We focus on the
organization of the system into modules, i.e., neuronal groups having
relatively higher connection density compared to that of the overall network.
We show that this mesoscopic feature cannot be explained exclusively in terms
of considerations, such as optimizing for resource constraints (viz., total
wiring cost) and communication efficiency (i.e., network path length).
Comparison with other complex networks designed for efficient transport (of
signals or resources) implies that neuronal networks form a distinct class.
This suggests that the principal function of the network, viz., processing of
sensory information resulting in appropriate motor response, may be playing a
vital role in determining the connection topology. Using modular spectral
analysis, we make explicit the intimate relation between function and structure
in the nervous system. This is further brought out by identifying functionally
critical neurons purely on the basis of patterns of intra- and inter-modular
connections. Our study reveals how the design of the nervous system reflects
several constraints, including its key functional role as a processor of
information.Comment: Published version, Minor modifications, 16 pages, 9 figure
Exploiting Temporal Complex Network Metrics in Mobile Malware Containment
Malicious mobile phone worms spread between devices via short-range Bluetooth
contacts, similar to the propagation of human and other biological viruses.
Recent work has employed models from epidemiology and complex networks to
analyse the spread of malware and the effect of patching specific nodes. These
approaches have adopted a static view of the mobile networks, i.e., by
aggregating all the edges that appear over time, which leads to an approximate
representation of the real interactions: instead, these networks are inherently
dynamic and the edge appearance and disappearance is highly influenced by the
ordering of the human contacts, something which is not captured at all by
existing complex network measures. In this paper we first study how the
blocking of malware propagation through immunisation of key nodes (even if
carefully chosen through static or temporal betweenness centrality metrics) is
ineffective: this is due to the richness of alternative paths in these
networks. Then we introduce a time-aware containment strategy that spreads a
patch message starting from nodes with high temporal closeness centrality and
show its effectiveness using three real-world datasets. Temporal closeness
allows the identification of nodes able to reach most nodes quickly: we show
that this scheme can reduce the cellular network resource consumption and
associated costs, achieving, at the same time, a complete containment of the
malware in a limited amount of time.Comment: 9 Pages, 13 Figures, In Proceedings of IEEE 12th International
Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM '11
From Network Structure to Dynamics and Back Again: Relating dynamical stability and connection topology in biological complex systems
The recent discovery of universal principles underlying many complex networks
occurring across a wide range of length scales in the biological world has
spurred physicists in trying to understand such features using techniques from
statistical physics and non-linear dynamics. In this paper, we look at a few
examples of biological networks to see how similar questions can come up in
very different contexts. We review some of our recent work that looks at how
network structure (e.g., its connection topology) can dictate the nature of its
dynamics, and conversely, how dynamical considerations constrain the network
structure. We also see how networks occurring in nature can evolve to modular
configurations as a result of simultaneously trying to satisfy multiple
structural and dynamical constraints. The resulting optimal networks possess
hubs and have heterogeneous degree distribution similar to those seen in
biological systems.Comment: 15 pages, 6 figures, to appear in Proceedings of "Dynamics On and Of
Complex Networks", ECSS'07 Satellite Workshop, Dresden, Oct 1-5, 200
Structural Properties of the Caenorhabditis elegans Neuronal Network
Despite recent interest in reconstructing neuronal networks, complete wiring
diagrams on the level of individual synapses remain scarce and the insights
into function they can provide remain unclear. Even for Caenorhabditis elegans,
whose neuronal network is relatively small and stereotypical from animal to
animal, published wiring diagrams are neither accurate nor complete and
self-consistent. Using materials from White et al. and new electron micrographs
we assemble whole, self-consistent gap junction and chemical synapse networks
of hermaphrodite C. elegans. We propose a method to visualize the wiring
diagram, which reflects network signal flow. We calculate statistical and
topological properties of the network, such as degree distributions, synaptic
multiplicities, and small-world properties, that help in understanding network
signal propagation. We identify neurons that may play central roles in
information processing and network motifs that could serve as functional
modules of the network. We explore propagation of neuronal activity in response
to sensory or artificial stimulation using linear systems theory and find
several activity patterns that could serve as substrates of previously
described behaviors. Finally, we analyze the interaction between the gap
junction and the chemical synapse networks. Since several statistical
properties of the C. elegans network, such as multiplicity and motif
distributions are similar to those found in mammalian neocortex, they likely
point to general principles of neuronal networks. The wiring diagram reported
here can help in understanding the mechanistic basis of behavior by generating
predictions about future experiments involving genetic perturbations, laser
ablations, or monitoring propagation of neuronal activity in response to
stimulation
A Network-Aware Distributed Membership Protocol for Collaborative Defense
To counteract current trends in network malware, distributed solutions have been developed that harness the power of collaborative end-host sensors. While these systems greatly increase the ability to defend against attack, this comes at the cost of complexity due to the coordination of distributed hosts across the dynamic network. Many previous solutions for distributed membership maintenance are agnostic to network conditions and have high overhead, making them less than ideal in the dynamic enterprise environment. In this work, we propose a network-aware, distributed membership protocol, CLUSTER, which improves the performance of the overlay system by biasing neighbor selection towards beneficial nodes based on multiple system metrics and network social patterns (of devices and their users). We provide an extensible method for aggregating and comparing multiple, possibly unrelated metrics. We demonstrate the effectiveness and utility of our protocol through simulation using real-world data and topologies. As part of our results, we highlight our analysis of node churn statistics, offering a new distribution to accurately model enterprise churn
Applications of Temporal Graph Metrics to Real-World Networks
Real world networks exhibit rich temporal information: friends are added and
removed over time in online social networks; the seasons dictate the
predator-prey relationship in food webs; and the propagation of a virus depends
on the network of human contacts throughout the day. Recent studies have
demonstrated that static network analysis is perhaps unsuitable in the study of
real world network since static paths ignore time order, which, in turn,
results in static shortest paths overestimating available links and
underestimating their true corresponding lengths. Temporal extensions to
centrality and efficiency metrics based on temporal shortest paths have also
been proposed. Firstly, we analyse the roles of key individuals of a corporate
network ranked according to temporal centrality within the context of a
bankruptcy scandal; secondly, we present how such temporal metrics can be used
to study the robustness of temporal networks in presence of random errors and
intelligent attacks; thirdly, we study containment schemes for mobile phone
malware which can spread via short range radio, similar to biological viruses;
finally, we study how the temporal network structure of human interactions can
be exploited to effectively immunise human populations. Through these
applications we demonstrate that temporal metrics provide a more accurate and
effective analysis of real-world networks compared to their static
counterparts.Comment: 25 page
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