4,086 research outputs found
Evolution of networks
We review the recent fast progress in statistical physics of evolving
networks. Interest has focused mainly on the structural properties of random
complex networks in communications, biology, social sciences and economics. A
number of giant artificial networks of such a kind came into existence
recently. This opens a wide field for the study of their topology, evolution,
and complex processes occurring in them. Such networks possess a rich set of
scaling properties. A number of them are scale-free and show striking
resilience against random breakdowns. In spite of large sizes of these
networks, the distances between most their vertices are short -- a feature
known as the ``small-world'' effect. We discuss how growing networks
self-organize into scale-free structures and the role of the mechanism of
preferential linking. We consider the topological and structural properties of
evolving networks, and percolation in these networks. We present a number of
models demonstrating the main features of evolving networks and discuss current
approaches for their simulation and analytical study. Applications of the
general results to particular networks in Nature are discussed. We demonstrate
the generic connections of the network growth processes with the general
problems of non-equilibrium physics, econophysics, evolutionary biology, etc.Comment: 67 pages, updated, revised, and extended version of review, submitted
to Adv. Phy
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
Genetic and pharmacological perturbation experiments, such as deleting a gene
and monitoring gene expression responses, are powerful tools for studying
cellular signal transduction pathways. However, it remains a challenge to
automatically derive knowledge of a cellular signaling system at a conceptual
level from systematic perturbation-response data. In this study, we explored a
framework that unifies knowledge mining and data mining approaches towards the
goal. The framework consists of the following automated processes: 1) applying
an ontology-driven knowledge mining approach to identify functional modules
among the genes responding to a perturbation in order to reveal potential
signals affected by the perturbation; 2) applying a graph-based data mining
approach to search for perturbations that affect a common signal with respect
to a functional module, and 3) revealing the architecture of a signaling system
organize signaling units into a hierarchy based on their relationships.
Applying this framework to a compendium of yeast perturbation-response data, we
have successfully recovered many well-known signal transduction pathways; in
addition, our analysis have led to many hypotheses regarding the yeast signal
transduction system; finally, our analysis automatically organized perturbed
genes as a graph reflecting the architect of the yeast signaling system.
Importantly, this framework transformed molecular findings from a gene level to
a conceptual level, which readily can be translated into computable knowledge
in the form of rules regarding the yeast signaling system, such as "if genes
involved in MAPK signaling are perturbed, genes involved in pheromone responses
will be differentially expressed"
Structure induction by lossless graph compression
This work is motivated by the necessity to automate the discovery of
structure in vast and evergrowing collection of relational data commonly
represented as graphs, for example genomic networks. A novel algorithm, dubbed
Graphitour, for structure induction by lossless graph compression is presented
and illustrated by a clear and broadly known case of nested structure in a DNA
molecule. This work extends to graphs some well established approaches to
grammatical inference previously applied only to strings. The bottom-up graph
compression problem is related to the maximum cardinality (non-bipartite)
maximum cardinality matching problem. The algorithm accepts a variety of graph
types including directed graphs and graphs with labeled nodes and arcs. The
resulting structure could be used for representation and classification of
graphs.Comment: 10 pages, 7 figures, 2 tables published in Proceedings of the Data
Compression Conference, 200
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