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.pdf
doi  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}
}

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