174 research outputs found
Hierarchical organization of modularity in metabolic networks
Spatially or chemically isolated functional modules composed of several
cellular components and carrying discrete functions are considered fundamental
building blocks of cellular organization, but their presence in highly
integrated biochemical networks lacks quantitative support. Here we show that
the metabolic networks of 43 distinct organisms are organized into many small,
highly connected topologic modules that combine in a hierarchical manner into
larger, less cohesive units, their number and degree of clustering following a
power law. Within Escherichia coli the uncovered hierarchical modularity
closely overlaps with known metabolic functions. The identified network
architecture may be generic to system-level cellular organization
Two-Peak and Three-Peak Optimal Complex Networks
A central issue in complex networks is tolerance to random failures and
intentional attacks. Current literature emphasizes the dichotomy between
networks with a power-law node connectivity distribution, which are robust to
random failures but fragile to targeted attacks, versus networks with an
exponentially decaying connectivity distribution, which are less tolerant to
failures but more resilient to attacks. We prove analytically that the optimal
network configuration under a classic measure of robustness is altogether
different from both of the above: in all cases, failure and/or attack, there
are no more than three distinct node connectivities in the optimal network
Matching experiments across species using expression values and textual information
Motivation: With the vast increase in the number of gene expression datasets deposited in public databases, novel techniques are required to analyze and mine this wealth of data. Similar to the way BLAST enables cross-species comparison of sequence data, tools that enable cross-species expression comparison will allow us to better utilize these datasets: cross-species expression comparison enables us to address questions in evolution and development, and further allows the identification of disease-related genes and pathways that play similar roles in humans and model organisms. Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species
Global organization of metabolic fluxes in the bacterium, Escherichia coli
Cellular metabolism, the integrated interconversion of thousands of metabolic
substrates through enzyme-catalyzed biochemical reactions, is the most
investigated complex intercellular web of molecular interactions. While the
topological organization of individual reactions into metabolic networks is
increasingly well understood, the principles governing their global functional
utilization under different growth conditions pose many open questions. We
implement a flux balance analysis of the E. coli MG1655 metabolism, finding
that the network utilization is highly uneven: while most metabolic reactions
have small fluxes, the metabolism's activity is dominated by several reactions
with very high fluxes. E. coli responds to changes in growth conditions by
reorganizing the rates of selected fluxes predominantly within this high flux
backbone. The identified behavior likely represents a universal feature of
metabolic activity in all cells, with potential implications to metabolic
engineering.Comment: 15 pages 4 figure
The topological relationship between the large-scale attributes and local interaction patterns of complex networks
Recent evidence indicates that the abundance of recurring elementary
interaction patterns in complex networks, often called subgraphs or motifs,
carry significant information about their function and overall organization.
Yet, the underlying reasons for the variable quantity of different subgraph
types, their propensity to form clusters, and their relationship with the
networks' global organization remain poorly understood. Here we show that a
network's large-scale topological organization and its local subgraph structure
mutually define and predict each other, as confirmed by direct measurements in
five well studied cellular networks. We also demonstrate the inherent existence
of two distinct classes of subgraphs, and show that, in contrast to the
low-density type II subgraphs, the highly abundant type I subgraphs cannot
exist in isolation but must naturally aggregate into subgraph clusters. The
identified topological framework may have important implications for our
understanding of the origin and function of subgraphs in all complex networks.Comment: pape
Lethality and centrality in protein networks
In this paper we present the first mathematical analysis of the protein
interaction network found in the yeast, S. cerevisiae. We show that, (a) the
identified protein network display a characteristic scale-free topology that
demonstrate striking similarity to the inherent organization of metabolic
networks in particular, and to that of robust and error-tolerant networks in
general. (b) the likelihood that deletion of an individual gene product will
prove lethal for the yeast cell clearly correlates with the number of
interactions the protein has, meaning that highly-connected proteins are more
likely to prove essential than proteins with low number of links to other
proteins. These results suggest that a scale-free architecture is a generic
property of cellular networks attributable to universal self-organizing
principles of robust and error-tolerant networks and that will likely to
represent a generic topology for protein-protein interactions.Comment: See also http:/www.nd.edu/~networks and
http:/www.nd.edu/~networks/cel
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