Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data.

@article{citeulike:96502, abstract = {MOTIVATION: High-throughput technologies have facilitated the acquisition of large genomics and proteomics datasets. 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. RESULTS: We demonstrate 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. Overall, the methods we propose are capable of deducing and quantifying functional interactions within and across cellular gene, signaling and metabolic networks. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.dbi.tju.edu/bioinformatics2004.pdf}, address = {Department of Mathematics, Rutgers University, Piscataway, NJ 08854, USA.}, author = {Sontag, E. and Kiyatkin, A. and Kholodenko, B. N. }, citeulike-article-id = {96502}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {microarrays, networks}, month = {August}, number = {12}, pages = {1877--1886}, posted-at = {2005-03-29 18:33:50}, priority = {2}, title = {Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15037511}, volume = {20}, year = {2004} }

TY - JOUR ID - citeulike:96502 L3 - citeulike-article-id:96502 TI - Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. JF - Bioinformatics VL - 20 IS - 12 SP - 1877 EP - 1886 AD - Department of Mathematics, Rutgers University, Piscataway, NJ 08854, USA. SN - 1367-4803 N2 - MOTIVATION: High-throughput technologies have facilitated the acquisition of large genomics and proteomics datasets. 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. RESULTS: We demonstrate 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. Overall, the methods we propose are capable of deducing and quantifying functional interactions within and across cellular gene, signaling and metabolic networks. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.dbi.tju.edu/bioinformatics2004.pdf KW - microarrays KW - networks AU - Sontag, E AU - Kiyatkin, A AU - Kholodenko, BN PY - 2004/08/12/ UR - http://view.ncbi.nlm.nih.gov/pubmed/15037511 ER -