@book{
 title = {Computational Systems Biology},
 type = {book},
 year = {2014},
 source = {Computational Systems Biology},
 keywords = {Bayesian network,Brute force,Circadian rhythms,Gene network,Greedy algorithm,Microarray,Petri net,Promoter regions},
 pages = {89-112},
 websites = {http://www.sciencedirect.com/science/article/pii/B978012405926900006X},
 publisher = {Elsevier},
 id = {61986292-4436-32b6-95fc-0f63bd3d96a3},
 created = {2015-04-11T19:52:01.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},
 last_modified = {2017-03-14T14:28:38.949Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This chapter describes the computational methods for estimating, modeling, and simulating biological systems. It also presents two approaches to understand biological systems and describes a method and a software tool developed by our research group. Bayesian network is a mathematical model for representing causal relationships among random variables by using conditional probabilities. The conditional probabilities describe the parent-child relationships and can be viewed as an extension of the deterministic models like Boolean networks. This model is suited for modeling qualitative relations between genes and allows mathematical and algorithmic analyses. We also devised a method to infer a gene network in terms of a linear system of differential equations from time-course gene expression data. A software tool is developed based on Petri net to modeling and simulation of gene networks. With this software tool, various models have been constructed and its utility has been demonstrated in practice.},
 bibtype = {book},
 author = {Kriete, Andres and Eils, Roland and Imoto, Seiya and Matsuno, Hiroshi and Miyano, Satoru},
 doi = {10.1016/B978-0-12-405926-9.00006-X}
}

{"_id":"GBiXde3HEadmGHohs","authorIDs":[],"author_short":["Kriete, A.","Eils, R.","Imoto, S.","Matsuno, H.","Miyano, S."],"bibbaseid":"kriete-eils-imoto-matsuno-miyano-computationalsystemsbiology-2014","bibdata":{"title":"Computational Systems Biology","type":"book","year":"2014","source":"Computational Systems Biology","keywords":"Bayesian network,Brute force,Circadian rhythms,Gene network,Greedy algorithm,Microarray,Petri net,Promoter regions","pages":"89-112","websites":"http://www.sciencedirect.com/science/article/pii/B978012405926900006X","publisher":"Elsevier","id":"61986292-4436-32b6-95fc-0f63bd3d96a3","created":"2015-04-11T19:52:01.000Z","accessed":"2015-04-11","file_attached":false,"profile_id":"95e10851-cdf3-31de-9f82-1ab629e601b0","group_id":"db853ba9-040a-35e0-8f4f-fe9d1f87c7b5","last_modified":"2017-03-14T14:28:38.949Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"private_publication":false,"abstract":"This chapter describes the computational methods for estimating, modeling, and simulating biological systems. It also presents two approaches to understand biological systems and describes a method and a software tool developed by our research group. Bayesian network is a mathematical model for representing causal relationships among random variables by using conditional probabilities. The conditional probabilities describe the parent-child relationships and can be viewed as an extension of the deterministic models like Boolean networks. This model is suited for modeling qualitative relations between genes and allows mathematical and algorithmic analyses. We also devised a method to infer a gene network in terms of a linear system of differential equations from time-course gene expression data. A software tool is developed based on Petri net to modeling and simulation of gene networks. With this software tool, various models have been constructed and its utility has been demonstrated in practice.","bibtype":"book","author":"Kriete, Andres and Eils, Roland and Imoto, Seiya and Matsuno, Hiroshi and Miyano, Satoru","doi":"10.1016/B978-0-12-405926-9.00006-X","bibtex":"@book{\n title = {Computational Systems Biology},\n type = {book},\n year = {2014},\n source = {Computational Systems Biology},\n keywords = {Bayesian network,Brute force,Circadian rhythms,Gene network,Greedy algorithm,Microarray,Petri net,Promoter regions},\n pages = {89-112},\n websites = {http://www.sciencedirect.com/science/article/pii/B978012405926900006X},\n publisher = {Elsevier},\n id = {61986292-4436-32b6-95fc-0f63bd3d96a3},\n created = {2015-04-11T19:52:01.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This chapter describes the computational methods for estimating, modeling, and simulating biological systems. It also presents two approaches to understand biological systems and describes a method and a software tool developed by our research group. Bayesian network is a mathematical model for representing causal relationships among random variables by using conditional probabilities. The conditional probabilities describe the parent-child relationships and can be viewed as an extension of the deterministic models like Boolean networks. This model is suited for modeling qualitative relations between genes and allows mathematical and algorithmic analyses. We also devised a method to infer a gene network in terms of a linear system of differential equations from time-course gene expression data. A software tool is developed based on Petri net to modeling and simulation of gene networks. With this software tool, various models have been constructed and its utility has been demonstrated in practice.},\n bibtype = {book},\n author = {Kriete, Andres and Eils, Roland and Imoto, Seiya and Matsuno, Hiroshi and Miyano, Satoru},\n doi = {10.1016/B978-0-12-405926-9.00006-X}\n}","author_short":["Kriete, A.","Eils, R.","Imoto, S.","Matsuno, H.","Miyano, S."],"urls":{"Website":"http://www.sciencedirect.com/science/article/pii/B978012405926900006X"},"biburl":"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0","bibbaseid":"kriete-eils-imoto-matsuno-miyano-computationalsystemsbiology-2014","role":"author","keyword":["Bayesian network","Brute force","Circadian rhythms","Gene network","Greedy algorithm","Microarray","Petri net","Promoter regions"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"book","biburl":"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0","creationDate":"2015-04-11T20:28:32.197Z","downloads":0,"keywords":["bayesian network","brute force","circadian rhythms","gene network","greedy algorithm","microarray","petri net","promoter regions"],"search_terms":["computational","systems","biology","kriete","eils","imoto","matsuno","miyano"],"title":"Computational Systems Biology","year":2014,"dataSources":["8hwkQbNZz66Dc7LzM","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}