dorsal/arxiv
View SchemaTopological Generalizations of network motifs
| Authors | N. Kashtan, S. Itzkovitz, R. Milo, U. Alon |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0312019 |
| URL | https://arxiv.org/abs/q-bio/0312019 |
| DOI | 10.1103/PhysRevE.70.031909 |
Abstract
Biological and technological networks contain patterns, termed network motifs, which occur far more often than in randomized networks. Network motifs were suggested to be elementary building blocks that carry out key functions in the network. It is of interest to understand how network motifs combine to form larger structures. To address this, we present a systematic approach to define 'motif generalizations': families of motifs of different sizes that share a common architectural theme. To define motif generalizations, we first define 'roles' in a subgraph according to structural equivalence. For example, the feedforward loop triad, a motif in transcription, neuronal and some electronic networks, has three roles, an input node, an output node and an internal node. The roles are used to define possible generalizations of the motif. The feedforward loop can have three simple generalizations, based on replicating each of the three roles and their connections. We present algorithms for efficiently detecting motif generalizations. We find that the transcription networks of bacteria and yeast display only one of the three generalizations, the multi-output feedforward generalization. In contrast, the neuronal network of \emph{C. elegans} mainly displays the multi-input generalization. Forward-logic electronic circuits display a multi-input, multi-output hybrid. Thus, networks which share a common motif can have very different generalizations of that motif. Using mathematical modelling, we describe the information processing functions of the different motif generalizations in transcription, neuronal and electronic networks.
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"abstract": "Biological and technological networks contain patterns, termed network\nmotifs, which occur far more often than in randomized networks. Network motifs\nwere suggested to be elementary building blocks that carry out key functions in\nthe network. It is of interest to understand how network motifs combine to form\nlarger structures. To address this, we present a systematic approach to define\n\u0027motif generalizations\u0027: families of motifs of different sizes that share a\ncommon architectural theme. To define motif generalizations, we first define\n\u0027roles\u0027 in a subgraph according to structural equivalence. For example, the\nfeedforward loop triad, a motif in transcription, neuronal and some electronic\nnetworks, has three roles, an input node, an output node and an internal node.\nThe roles are used to define possible generalizations of the motif. The\nfeedforward loop can have three simple generalizations, based on replicating\neach of the three roles and their connections. We present algorithms for\nefficiently detecting motif generalizations. We find that the transcription\nnetworks of bacteria and yeast display only one of the three generalizations,\nthe multi-output feedforward generalization. In contrast, the neuronal network\nof \\emph{C. elegans} mainly displays the multi-input generalization.\nForward-logic electronic circuits display a multi-input, multi-output hybrid.\nThus, networks which share a common motif can have very different\ngeneralizations of that motif. Using mathematical modelling, we describe the\ninformation processing functions of the different motif generalizations in\ntranscription, neuronal and electronic networks.",
"arxiv_id": "q-bio/0312019",
"authors": [
"N. Kashtan",
"S. Itzkovitz",
"R. Milo",
"U. Alon"
],
"categories": [
"q-bio.MN",
"cond-mat.stat-mech"
],
"doi": "10.1103/PhysRevE.70.031909",
"title": "Topological Generalizations of network motifs",
"url": "https://arxiv.org/abs/q-bio/0312019"
},
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