dorsal/arxiv
View SchemaFunctional cartography of complex metabolic networks
| Authors | Roger Guimera, Luis A. Nunes Amaral |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0502035 |
| URL | https://arxiv.org/abs/q-bio/0502035 |
| DOI | 10.1038/nature03288 |
| Journal | Nature 433, 895-900 (2005) |
Abstract
High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ``cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.
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"abstract": "High-throughput techniques are leading to an explosive growth in the size of\nbiological databases and creating the opportunity to revolutionize our\nunderstanding of life and disease. Interpretation of these data remains,\nhowever, a major scientific challenge. Here, we propose a methodology that\nenables us to extract and display information contained in complex networks.\nSpecifically, we demonstrate that one can (i) find functional modules in\ncomplex networks, and (ii) classify nodes into universal roles according to\ntheir pattern of intra- and inter-module connections. The method thus yields a\n``cartographic representation\u0027\u0027 of complex networks. Metabolic networks are\namong the most challenging biological networks and, arguably, the ones with\nmore potential for immediate applicability. We use our method to analyze the\nmetabolic networks of twelve organisms from three different super-kingdoms. We\nfind that, typically, 80% of the nodes are only connected to other nodes within\ntheir respective modules, and that nodes with different roles are affected by\ndifferent evolutionary constraints and pressures. Remarkably, we find that\nlow-degree metabolites that connect different modules are more conserved than\nhubs whose links are mostly within a single module.",
"arxiv_id": "q-bio/0502035",
"authors": [
"Roger Guimera",
"Luis A. Nunes Amaral"
],
"categories": [
"q-bio.MN",
"cond-mat.dis-nn"
],
"doi": "10.1038/nature03288",
"journal_ref": "Nature 433, 895-900 (2005)",
"title": "Functional cartography of complex metabolic networks",
"url": "https://arxiv.org/abs/q-bio/0502035"
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