436 research outputs found
Modular networks emerge from multiconstraint optimization
Modular structure is ubiquitous among complex networks. We note that most
such systems are subject to multiple structural and functional constraints,
e.g., minimizing the average path length and the total number of links, while
maximizing robustness against perturbations in node activity. We show that the
optimal networks satisfying these three constraints are characterized by the
existence of multiple subnetworks (modules) sparsely connected to each other.
In addition, these modules have distinct hubs, resulting in an overall
heterogeneous degree distribution.Comment: 5 pages, 4 figures; Published versio
Familial hematuria
Hematuria is a common presenting complaint in pediatric nephrology clinics and often has a familial basis. This teaching article provides an overview of causes, diagnosis, and management of the major forms of familial hematuria, Alport syndrome, and thin basement membrane nephropathy
Subgraphs and network motifs in geometric networks
Many real-world networks describe systems in which interactions decay with
the distance between nodes. Examples include systems constrained in real space
such as transportation and communication networks, as well as systems
constrained in abstract spaces such as multivariate biological or economic
datasets and models of social networks. These networks often display network
motifs: subgraphs that recur in the network much more often than in randomized
networks. To understand the origin of the network motifs in these networks, it
is important to study the subgraphs and network motifs that arise solely from
geometric constraints. To address this, we analyze geometric network models, in
which nodes are arranged on a lattice and edges are formed with a probability
that decays with the distance between nodes. We present analytical solutions
for the numbers of all 3 and 4-node subgraphs, in both directed and
non-directed geometric networks. We also analyze geometric networks with
arbitrary degree sequences, and models with a field that biases for directed
edges in one direction. Scaling rules for scaling of subgraph numbers with
system size, lattice dimension and interaction range are given. Several
invariant measures are found, such as the ratio of feedback and feed-forward
loops, which do not depend on system size, dimension or connectivity function.
We find that network motifs in many real-world networks, including social
networks and neuronal networks, are not captured solely by these geometric
models. This is in line with recent evidence that biological network motifs
were selected as basic circuit elements with defined information-processing
functions.Comment: 9 pages, 6 figure
Statistical significance of rich-club phenomena in complex networks
We propose that the rich-club phenomena in complex networks should be defined
in the spirit of bootstrapping, in which a null model is adopted to assess the
statistical significance of the rich-club detected. Our method can be served as
a definition of rich-club phenomenon and is applied to analyzing three real
networks and three model networks. The results improve significantly compared
with previously reported results. We report a dilemma with an exceptional
example, showing that there does not exist an omnipotent definition for the
rich-club phenomenon.Comment: 3 Revtex pages + 5 figure
Evaluating Local Community Methods in Networks
We present a new benchmarking procedure that is unambiguous and specific to
local community-finding methods, allowing one to compare the accuracy of
various methods. We apply this to new and existing algorithms. A simple class
of synthetic benchmark networks is also developed, capable of testing
properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc
Guided evolution of in silico microbial populations in complex environments accelerates evolutionary rates through a step-wise adaptation
Abstract Background During their lifetime, microbes are exposed to environmental variations, each with its distinct spatio-temporal dynamics. Microbial communities display a remarkable degree of phenotypic plasticity, and highly-fit individuals emerge quite rapidly during microbial adaptation to novel environments. However, there exists a high variability when it comes to adaptation potential, and while adaptation occurs rapidly in certain environmental transitions, in others organisms struggle to adapt. Here, we investigate the hypothesis that the rate of evolution can both increase or decrease, depending on the similarity and complexity of the intermediate and final environments. Elucidating such dependencies paves the way towards controlling the rate and direction of evolution, which is of interest to industrial and medical applications. Results Our results show that the rate of evolution can be accelerated by evolving cell populations in sequential combinations of environments that are increasingly more complex. To quantify environmental complexity, we evaluate various information-theoretic metrics, and we provide evidence that multivariate mutual information between environmental signals in a given environment correlates well with the rate of evolution in that environment, as measured in our simulations. We find that strong positive and negative correlations between the intermediate and final environments lead to the increase of evolutionary rates, when the environmental complexity increases. Horizontal Gene Transfer is shown to further augment this acceleration, under certain conditions. Interestingly, our simulations show that weak environmental correlations lead to deceleration of evolution, regardless of environmental complexity. Further analysis of network evolution provides a mechanistic explanation of this phenomenon, as exposing cells to intermediate environments can trap the population to local neighborhoods of sub-optimal fitness
Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience
Physical symbol systems are needed for open-ended cognition. A good way to
understand physical symbol systems is by comparison of thought to chemistry.
Both have systematicity, productivity and compositionality. The state of the
art in cognitive architectures for open-ended cognition is critically assessed.
I conclude that a cognitive architecture that evolves symbol structures in the
brain is a promising candidate to explain open-ended cognition. Part 2 of the
paper presents such a cognitive architecture.Comment: Darwinian Neurodynamics. Submitted as a two part paper to Living
Machines 2013 Natural History Museum, Londo
FROM A RESEARCH PROTOTYPE TO A DEPLOYED SYSTEM: THE CASE OF THE HECHT MUSEUM VISITOR\u27S GUIDE
Cultural heritage, Deployment, Mobile guides, Research prototype
Coarse-Graining and Self-Dissimilarity of Complex Networks
Can complex engineered and biological networks be coarse-grained into smaller
and more understandable versions in which each node represents an entire
pattern in the original network? To address this, we define coarse-graining
units (CGU) as connectivity patterns which can serve as the nodes of a
coarse-grained network, and present algorithms to detect them. We use this
approach to systematically reverse-engineer electronic circuits, forming
understandable high-level maps from incomprehensible transistor wiring: first,
a coarse-grained version in which each node is a gate made of several
transistors is established. Then, the coarse-grained network is itself
coarse-grained, resulting in a high-level blueprint in which each node is a
circuit-module made of multiple gates. We apply our approach also to a
mammalian protein-signaling network, to find a simplified coarse-grained
network with three main signaling channels that correspond to cross-interacting
MAP-kinase cascades. We find that both biological and electronic networks are
'self-dissimilar', with different network motifs found at each level. The
present approach can be used to simplify a wide variety of directed and
nondirected, natural and designed networks.Comment: 11 pages, 11 figure
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