Growing genetic regulatory networks from seed genes
Motivation: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network...
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| Veröffentlicht in: | Bioinformatics 2004-05, Vol.20 (8), p.1241-1247 |
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| creator | Hashimoto, Ronaldo F. Kim, Seungchan Shmulevich, Ilya Zhang, Wei Bittner, Michael L. Dougherty, Edward R. |
| description | Motivation: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. Results: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. Availability: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm |
| doi_str_mv | 10.1093/bioinformatics/bth074 |
| format | Article |
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In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. Results: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. Availability: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/bth074</identifier><identifier>PMID: 14871865</identifier><identifier>CODEN: BOINFP</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>Algorithms ; Animals ; Biological and medical sciences ; Computer Simulation ; Evolution, Molecular ; Fundamental and applied biological sciences. 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Data processing in biology (general aspects) ; Melanoma - genetics ; Models, Genetic ; Models, Statistical ; Signal Transduction - genetics ; Transcription, Genetic - genetics</subject><ispartof>Bioinformatics, 2004-05, Vol.20 (8), p.1241-1247</ispartof><rights>2005 INIST-CNRS</rights><rights>Copyright Oxford University Press(England) May 22, 2004</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-8ee2de351cd6adaf578911509dbcd1a8230538d481de441fb9326c1d2ce838053</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15889447$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/14871865$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hashimoto, Ronaldo F.</creatorcontrib><creatorcontrib>Kim, Seungchan</creatorcontrib><creatorcontrib>Shmulevich, Ilya</creatorcontrib><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Bittner, Michael L.</creatorcontrib><creatorcontrib>Dougherty, Edward R.</creatorcontrib><title>Growing genetic regulatory networks from seed genes</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Motivation: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. Results: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. Availability: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm</description><subject>Algorithms</subject><subject>Animals</subject><subject>Biological and medical sciences</subject><subject>Computer Simulation</subject><subject>Evolution, Molecular</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Regulation - physiology</subject><subject>General aspects</subject><subject>Genetic Variation</subject><subject>Glioma - genetics</subject><subject>Humans</subject><subject>Mathematics in biology. Statistical analysis. Models. Metrology. 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| subjects | Algorithms Animals Biological and medical sciences Computer Simulation Evolution, Molecular Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods Gene Expression Regulation - physiology General aspects Genetic Variation Glioma - genetics Humans Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Melanoma - genetics Models, Genetic Models, Statistical Signal Transduction - genetics Transcription, Genetic - genetics |
| title | Growing genetic regulatory networks from seed genes |
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