Reconstructing dynamic regulatory maps Export

@article{citeulike:1050449, abstract = {Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.}, address = {Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.}, author = {Ernst, Jason and Vainas, Oded and Harbison, Christopher T. and Simon, Itamar and Bar-Joseph, Ziv}, citeulike-article-id = {1050449}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/msb4100115}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/msb4100115}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17224918}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17224918}, day = {16}, doi = {10.1038/msb4100115}, issn = {1744-4292}, journal = {Molecular Systems Biology}, keywords = {dynamic, map, regulatory}, month = {January}, posted-at = {2007-02-13 19:09:13}, priority = {3}, publisher = {Nature Publishing Group}, title = {Reconstructing dynamic regulatory maps}, url = {http://dx.doi.org/10.1038/msb4100115}, volume = {3}, year = {2007} }

TY - JOUR ID - citeulike:1050449 L3 - citeulike-article-id:1050449 N2 - Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation. JF - Molecular Systems Biology SN - 1744-4292 AD - Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. TI - Reconstructing dynamic regulatory maps PB - Nature Publishing Group VL - 3 KW - dynamic KW - map KW - regulatory AU - Ernst, Jason AU - Vainas, Oded AU - Harbison, Christopher AU - Simon, Itamar AU - Bar-Joseph, Ziv PY - 2007/01/16/ UR - http://dx.doi.org/10.1038/msb4100115 ER -