Topological Generalizations of network motifs Export

@misc{citeulike:98, 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.}, archivePrefix = {arXiv}, author = {Kashtan, N. and Itzkovitz, S. and Milo, R. and Alon, U.}, citeulike-article-id = {98}, citeulike-linkout-0 = {http://arxiv.org/abs/q-bio.MN/0312019}, citeulike-linkout-1 = {http://arxiv.org/pdf/q-bio.MN/0312019}, comment = {If you take a bog standard motif, like the feed forward loop on three nodes (X->Y, Y->Z, and X->Z) and then duplicate node Z a few times to produce a feed forward loop with multiple outputs, then it turns out that you'll find quite a lot of them in the E. coli gene regulation network. It makes you wonder whether these networks actually contain quite large motifs, but we just haven't got a powerful enough computer program to find them? ---=note-separator=--- * Are motifs independent, or do they combine to form larger structures * Computationally nasty * ''Motif generalization'' are motifs stuck to each other. E.g. four-node generalizations of the FFL. These can be got be node duplication. * He defines a ''role'' to be basically a subgraph isomorphism * The ''multi-z FFLs'' (the things you get if you duplicate the output node a few times) are high significant in ''E. coli''. Not the multi-y or the multi-x FFLs though. In fact they don't occur at all. * In ''C. elegans'' brain, this time multi-x is significant * Don't find these simple generalizations in electronic circuits, but you do see a multi-x and multi-z generalization. * He analyzes the dynamics of multi-z with positive interactions. * * Obviously you get temporal ordering of outputs * * Persistence detector functionality of original FFL is still there * The multi-x dynamics are such that Y remembers the state during its relaxation time, so two short pulses (one on each input) can activate the output where only one pulse wouldn't. * Appendix has details of formal motif definitions and algorithms to find motif generalizations }, day = {16}, eprint = {q-bio.MN/0312019}, keywords = {bigmotifs, motifs, networks}, month = {May}, posted-at = {2004-11-07 13:58:29}, title = {Topological Generalizations of network motifs}, url = {http://arxiv.org/abs/q-bio.MN/0312019}, year = {2004} }

TY - ELEC ID - citeulike:98 L3 - citeulike-article-id:98 N2 - 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. TI - Topological Generalizations of network motifs KW - bigmotifs KW - motifs KW - networks N1 - If you take a bog standard motif, like the feed forward loop on three nodes (X->Y, Y->Z, and X->Z) and then duplicate node Z a few times to produce a feed forward loop with multiple outputs, then it turns out that you'll find quite a lot of them in the E. coli gene regulation network. It makes you wonder whether these networks actually contain quite large motifs, but we just haven't got a powerful enough computer program to find them? N1 - * Are motifs independent, or do they combine to form larger structures * Computationally nasty * ''Motif generalization'' are motifs stuck to each other. E.g. four-node generalizations of the FFL. These can be got be node duplication. * He defines a ''role'' to be basically a subgraph isomorphism * The ''multi-z FFLs'' (the things you get if you duplicate the output node a few times) are high significant in ''E. coli''. Not the multi-y or the multi-x FFLs though. In fact they don't occur at all. * In ''C. elegans'' brain, this time multi-x is significant * Don't find these simple generalizations in electronic circuits, but you do see a multi-x and multi-z generalization. * He analyzes the dynamics of multi-z with positive interactions. * * Obviously you get temporal ordering of outputs * * Persistence detector functionality of original FFL is still there * The multi-x dynamics are such that Y remembers the state during its relaxation time, so two short pulses (one on each input) can activate the output where only one pulse wouldn't. * Appendix has details of formal motif definitions and algorithms to find motif generalizations AU - Kashtan, N AU - Itzkovitz, S AU - Milo, R AU - Alon, U PY - 2004/05/16/ UR - http://arxiv.org/abs/q-bio.MN/0312019 ER -