Superfamilies of Evolved and Designed Networks

Milo, Ron; Itzkovitz, Shalev; Kashtan, Nadav; Levitt, Reuven; Shen-Orr, Shai S.; Ayzenshtat, Inbal; Sheffer, Michal; Alon, Uri

doi:10.1126/science.1089167

Cited by 1,228 publications

(1,349 citation statements)

References 24 publications

Supporting

Mentioning

1,311

Contrasting

Unclassified

Order By: Relevance

“…On the next higher level of organization -neural circuits linking small sets of connected brain areas -the approach of motif analysis can be used to identify patterns of local interconnections that occur with a significantly higher frequency in real networks than in randomized networks of the same size [29,30,31]. Biological and technological networks contain several characteristic motifs, such as 'feedforward loops' and 'bi-parallel pathways'.…”

Section: Structural Contributions Of Individual Areas and Motifsmentioning

confidence: 99%

Organization, development and function of complex brain networks

Sporns

Chialvo

Kaiser

et al. 2004

Trends in Cognitive Sciences

1,848

1,358

View full text Add to dashboard Cite

Section: Structural Contributions Of Individual Areas and Motifsmentioning

confidence: 99%

Organization, development and function of complex brain networks

Sporns

Chialvo

Kaiser

et al. 2004

Trends in Cognitive Sciences

1,848

1,358

View full text Add to dashboard Cite

“…Closed walks are related to the network subgraph [26,31]. Thus, SC accounts for the number of subgraphs in which a protein participates, giving more weights to smaller subgraphs, which have been previously identified as important structural motifs in biological networks [32][33][34].…”

Section: ( )mentioning

confidence: 99%

Virtual identification of essential proteins within the protein interaction network of yeast

2006

View full text Add to dashboard Cite

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organisational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the non-essential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.

show abstract

“…To handle this issue one can consider instead of the z-scores defined above, a so-called significance profile 26 defined by…”

Section: Motif Frequency Vectorsmentioning

confidence: 99%

“…The motif significance profile allows for direct comparisons between networks of different sizes. This is important due to the fact that motifs in larger networks tend to exhibit larger z-scores than they do in smaller networks 23,26 . Note also, that each entry of the motif significance profile lies in the interval [−1, 1].…”

Section: Motif Frequency Vectorsmentioning

confidence: 99%

Network motif frequency vectors reveal evolving metabolic network organisation

2015

View full text Add to dashboard Cite

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

show abstract

Superfamilies of Evolved and Designed Networks

Cited by 1,228 publications

References 24 publications

Organization, development and function of complex brain networks

Organization, development and function of complex brain networks

Virtual identification of essential proteins within the protein interaction network of yeast

Network motif frequency vectors reveal evolving metabolic network organisation

Contact Info

Product

Resources

About