dorsal/arxiv
View SchemaLarge-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis
| Authors | C. Christensen, A. Gupta, C. D. Maranas, R. Albert |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0607024 |
| URL | https://arxiv.org/abs/q-bio/0607024 |
| DOI | 10.1016/j.physa.2006.04.118 |
Abstract
We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.
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"abstract": "We present the methods and results of a two-stage modeling process that\ngenerates candidate gene-regulatory networks of the bacterium B. subtilis from\nexperimentally obtained, yet mathematically underdetermined microchip array\ndata. By employing a computational, linear correlative procedure to generate\nthese networks, and by analyzing the networks from a graph theoretical\nperspective, we are able to verify the biological viability of our inferred\nnetworks, and we demonstrate that our networks\u0027 graph theoretical properties\nare remarkably similar to those of other biological systems. In addition, by\ncomparing our inferred networks to those of a previous, noisier implementation\nof the linear inference process [17], we are able to identify trends in graph\ntheoretical behavior that occur both in our networks as well as in their\nperturbed counterparts. These commonalities in behavior at multiple levels of\ncomplexity allow us to ascertain the level of complexity to which our process\nis robust to noise.",
"arxiv_id": "q-bio/0607024",
"authors": [
"C. Christensen",
"A. Gupta",
"C. D. Maranas",
"R. Albert"
],
"categories": [
"q-bio.MN",
"cond-mat.stat-mech",
"q-bio.SC"
],
"doi": "10.1016/j.physa.2006.04.118",
"title": "Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis",
"url": "https://arxiv.org/abs/q-bio/0607024"
},
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