Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network. Export

@article{Zak2003, abstract = {Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.}, address = {Department of Chemical Engineering, University of Delaware, Newark, Delaware 19716, USA.}, author = {Zak, D. E. and Gonye, G. E. and Schwaber, J. S. and Doyle, F. J.}, citeulike-article-id = {399269}, citeulike-linkout-0 = {http://dx.doi.org/10.1101/gr.1198103}, citeulike-linkout-1 = {http://www.genome.org/cgi/content/abstract/13/11/2396}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/14597654}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=14597654}, doi = {10.1101/gr.1198103}, issn = {1088-9051}, journal = {Genome Res}, keywords = {identifiability}, month = {November}, number = {11}, pages = {2396--2405}, posted-at = {2005-11-17 20:52:37}, priority = {4}, title = {Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.}, url = {http://dx.doi.org/10.1101/gr.1198103}, volume = {13}, year = {2003} }

TY - JOUR ID - Zak2003 L3 - citeulike-article-id:399269 N2 - Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems. IS - 11 JF - Genome Res SN - 1088-9051 AD - Department of Chemical Engineering, University of Delaware, Newark, Delaware 19716, USA. EP - 2405 TI - Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network. VL - 13 SP - 2396 KW - identifiability AU - Zak, DE AU - Gonye, GE AU - Schwaber, JS AU - Doyle, FJ PY - 2003/11// UR - http://dx.doi.org/10.1101/gr.1198103 ER -