dorsal/arxiv
View SchemaConditional Network Analysis Identifies Candidate Regulator Genes in Human B Cells
| Authors | Kai Wang, Nilanjana Banerjee, Adam Margolin, Ilya Nemenman, Katia Basso, Riccardo Favera, Andrea Califano |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0411003 |
| URL | https://arxiv.org/abs/q-bio/0411003 |
Abstract
Cellular phenotypes are determined by the dynamical activity of networks of co-regulated genes. Elucidating such networks is crucial for the understanding of normal cell physiology as well as for the dissection of complex pathologic phenotypes. Existing methods for such "reverse engineering" of genetic networks from microarray expression data have been successful only in prokaryotes (E. coli) and lower eukaryotes (S. cerevisiae) with relatively simple genomes. Additionally, they have mostly attempted to reconstruct average properties about the network connectivity without capturing the highly conditional nature of the interactions. In this paper we extend the ARACNE algorithm, which we recently introduced and successfully applied to the reconstruction of whole-genome transcriptional networks from mammalian cells, precisely to link the existence of specific network structures to the expression or lack thereof of specific regulator genes. This is accomplished by analyzing thousands of alternative network topologies generated by constraining the data set on the presence or absence of putative regulator genes. By considering interactions that are consistently supported across several such constraints, we identify many transcriptional interactions that would not have been detectable by the original method. By selecting genes that produce statistically significant changes in network topology, we identify novel candidate regulator genes. Further analysis shows that transcription factors, kinases, phosphatases, and other gene families known to effect biochemical interactions, are significantly overrepresented among the set of candidate regulator genes identified in silico, indirectly supporting the validity of the approach.
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"abstract": "Cellular phenotypes are determined by the dynamical activity of networks of\nco-regulated genes. Elucidating such networks is crucial for the understanding\nof normal cell physiology as well as for the dissection of complex pathologic\nphenotypes. Existing methods for such \"reverse engineering\" of genetic networks\nfrom microarray expression data have been successful only in prokaryotes (E.\ncoli) and lower eukaryotes (S. cerevisiae) with relatively simple genomes.\nAdditionally, they have mostly attempted to reconstruct average properties\nabout the network connectivity without capturing the highly conditional nature\nof the interactions. In this paper we extend the ARACNE algorithm, which we\nrecently introduced and successfully applied to the reconstruction of\nwhole-genome transcriptional networks from mammalian cells, precisely to link\nthe existence of specific network structures to the expression or lack thereof\nof specific regulator genes. This is accomplished by analyzing thousands of\nalternative network topologies generated by constraining the data set on the\npresence or absence of putative regulator genes. By considering interactions\nthat are consistently supported across several such constraints, we identify\nmany transcriptional interactions that would not have been detectable by the\noriginal method. By selecting genes that produce statistically significant\nchanges in network topology, we identify novel candidate regulator genes.\nFurther analysis shows that transcription factors, kinases, phosphatases, and\nother gene families known to effect biochemical interactions, are significantly\noverrepresented among the set of candidate regulator genes identified in\nsilico, indirectly supporting the validity of the approach.",
"arxiv_id": "q-bio/0411003",
"authors": [
"Kai Wang",
"Nilanjana Banerjee",
"Adam Margolin",
"Ilya Nemenman",
"Katia Basso",
"Riccardo Favera",
"Andrea Califano"
],
"categories": [
"q-bio.MN",
"q-bio.GN",
"q-bio.QM"
],
"title": "Conditional Network Analysis Identifies Candidate Regulator Genes in Human B Cells",
"url": "https://arxiv.org/abs/q-bio/0411003"
},
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