Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Sontag, E., Kiyatkin, A., & Kholodenko, B. Bioinformatics, 20(12):1877–1886, Oxford University Press, Oxford, UK, 2004. Supplementary materials are here: http://www.math.rutgers.edu/(tilde)sontag/FTPDIR/sontag-kiyatkin-kholodenko-informatics04-supplement.pdfdoi abstract bibtex High-throughput technologies have facilitated the acquisition of large genomics and proteomics data sets. However, these data provide snapshots of cellular behavior, rather than help us reveal causal relations. Here, we propose how these technologies can be utilized to infer the topology and strengths of connections among genes, proteins, and metabolites by monitoring time-dependent responses of cellular networks to experimental interventions. We show that all connections leading to a given network node, e.g., to a particular gene, can be deduced from responses to perturbations none of which directly influences that node, e.g., using strains with knock-outs to other genes. To infer all interactions from stationary data, each node should be perturbed separately or in combination with other nodes. Monitoring time series provides richer information and does not require perturbations to all nodes.
@ARTICLE{SontagKiyatinKholodenko04,
AUTHOR = {E.D. Sontag and A. Kiyatkin and B.N. Kholodenko},
JOURNAL = {Bioinformatics},
TITLE = {Inferring dynamic architecture of cellular networks
using time series of gene expression, protein and metabolite data},
YEAR = {2004},
OPTMONTH = {},
NOTE = {Supplementary materials are here: http://www.math.rutgers.edu/(tilde)sontag/FTPDIR/sontag-kiyatkin-kholodenko-informatics04-supplement.pdf},
NUMBER = {12},
PAGES = {1877--1886},
VOLUME = {20},
ADDRESS = {Oxford, UK},
KEYWORDS = {systems biology, biochemical networks,
systems identification, gene and protein networks,
reverse engineering},
PUBLISHER = {Oxford University Press},
PDF = {../../FTPDIR/sontag_kiyatkin_kholodenko_informatics04.pdf},
ABSTRACT = { High-throughput technologies have facilitated the
acquisition of large genomics and proteomics data sets. However,
these data provide snapshots of cellular behavior, rather than help
us reveal causal relations. Here, we propose how these technologies
can be utilized to infer the topology and strengths of connections
among genes, proteins, and metabolites by monitoring time-dependent
responses of cellular networks to experimental interventions. We show
that all connections leading to a given network node, e.g., to a
particular gene, can be deduced from responses to perturbations none
of which directly influences that node, e.g., using strains with
knock-outs to other genes. To infer all interactions from stationary
data, each node should be perturbed separately or in combination with
other nodes. Monitoring time series provides richer information and
does not require perturbations to all nodes. },
DOI = {http://dx.doi.org/10.1093/bioinformatics/bth173}
}
Downloads: 0
{"_id":"tkQyQKEqhT6aiX5ie","bibbaseid":"sontag-kiyatkin-kholodenko-inferringdynamicarchitectureofcellularnetworksusingtimeseriesofgeneexpressionproteinandmetabolitedata-2004","downloads":0,"creationDate":"2018-10-18T05:07:06.232Z","title":"Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data","author_short":["Sontag, E.","Kiyatkin, A.","Kholodenko, B."],"year":2004,"bibtype":"article","biburl":"http://www.sontaglab.org/PUBDIR/Biblio/complete-bibliography.bib","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["E.D."],"propositions":[],"lastnames":["Sontag"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Kiyatkin"],"suffixes":[]},{"firstnames":["B.N."],"propositions":[],"lastnames":["Kholodenko"],"suffixes":[]}],"journal":"Bioinformatics","title":"Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data","year":"2004","optmonth":"","note":"Supplementary materials are here: http://www.math.rutgers.edu/(tilde)sontag/FTPDIR/sontag-kiyatkin-kholodenko-informatics04-supplement.pdf","number":"12","pages":"1877–1886","volume":"20","address":"Oxford, UK","keywords":"systems biology, biochemical networks, systems identification, gene and protein networks, reverse engineering","publisher":"Oxford University Press","pdf":"../../FTPDIR/sontag_kiyatkin_kholodenko_informatics04.pdf","abstract":"High-throughput technologies have facilitated the acquisition of large genomics and proteomics data sets. However, these data provide snapshots of cellular behavior, rather than help us reveal causal relations. Here, we propose how these technologies can be utilized to infer the topology and strengths of connections among genes, proteins, and metabolites by monitoring time-dependent responses of cellular networks to experimental interventions. We show that all connections leading to a given network node, e.g., to a particular gene, can be deduced from responses to perturbations none of which directly influences that node, e.g., using strains with knock-outs to other genes. To infer all interactions from stationary data, each node should be perturbed separately or in combination with other nodes. Monitoring time series provides richer information and does not require perturbations to all nodes. ","doi":"http://dx.doi.org/10.1093/bioinformatics/bth173","bibtex":"@ARTICLE{SontagKiyatinKholodenko04,\n AUTHOR = {E.D. Sontag and A. Kiyatkin and B.N. Kholodenko},\n JOURNAL = {Bioinformatics},\n TITLE = {Inferring dynamic architecture of cellular networks \n using time series of gene expression, protein and metabolite data},\n YEAR = {2004},\n OPTMONTH = {},\n NOTE = {Supplementary materials are here: http://www.math.rutgers.edu/(tilde)sontag/FTPDIR/sontag-kiyatkin-kholodenko-informatics04-supplement.pdf},\n NUMBER = {12},\n PAGES = {1877--1886},\n VOLUME = {20},\n ADDRESS = {Oxford, UK},\n KEYWORDS = {systems biology, biochemical networks, \n systems identification, gene and protein networks, \n reverse engineering},\n PUBLISHER = {Oxford University Press},\n PDF = {../../FTPDIR/sontag_kiyatkin_kholodenko_informatics04.pdf},\n ABSTRACT = { High-throughput technologies have facilitated the \n acquisition of large genomics and proteomics data sets. However, \n these data provide snapshots of cellular behavior, rather than help \n us reveal causal relations. Here, we propose how these technologies \n can be utilized to infer the topology and strengths of connections \n among genes, proteins, and metabolites by monitoring time-dependent \n responses of cellular networks to experimental interventions. We show \n that all connections leading to a given network node, e.g., to a \n particular gene, can be deduced from responses to perturbations none \n of which directly influences that node, e.g., using strains with \n knock-outs to other genes. To infer all interactions from stationary \n data, each node should be perturbed separately or in combination with \n other nodes. Monitoring time series provides richer information and \n does not require perturbations to all nodes. },\n DOI = {http://dx.doi.org/10.1093/bioinformatics/bth173}\n}\n\n","author_short":["Sontag, E.","Kiyatkin, A.","Kholodenko, B."],"key":"SontagKiyatinKholodenko04","id":"SontagKiyatinKholodenko04","bibbaseid":"sontag-kiyatkin-kholodenko-inferringdynamicarchitectureofcellularnetworksusingtimeseriesofgeneexpressionproteinandmetabolitedata-2004","role":"author","urls":{},"keyword":["systems biology","biochemical networks","systems identification","gene and protein networks","reverse engineering"],"downloads":0,"html":""},"search_terms":["inferring","dynamic","architecture","cellular","networks","using","time","series","gene","expression","protein","metabolite","data","sontag","kiyatkin","kholodenko"],"keywords":["systems biology","biochemical networks","systems identification","gene and protein networks","reverse engineering"],"authorIDs":["5bc814f9db768e100000015a"],"dataSources":["DKqZbTmd7peqE4THw"]}