Computational methods for transcriptional regulation.

by: ED Siggia

Curr Opin Genet Dev, Vol. 15, No. 2. (April 2005), pp. 214-221.

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Abstract

How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional.

@article{citeulike:816984, abstract = {How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional.}, address = {Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. siggiae@rockefeller.edu}, author = {Siggia, E. D. }, citeulike-article-id = {816984}, doi = {http://dx.doi.org/10.1016/j.gde.2005.02.004}, issn = {0959-437X}, journal = {Curr Opin Genet Dev}, keywords = {cell-models, gene-expression, genome-transcriptome, modeling-and-simulation, molecular-signaling, physiological-remodeling, regulatory-cascades}, month = {April}, number = {2}, pages = {214--221}, posted-at = {2008-07-29 20:00:41}, priority = {2}, title = {Computational methods for transcriptional regulation.}, url = {http://dx.doi.org/10.1016/j.gde.2005.02.004}, volume = {15}, year = {2005} }

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TY - JOUR ID - citeulike:816984 L3 - citeulike-article-id:816984 TI - Computational methods for transcriptional regulation. JF - Curr Opin Genet Dev VL - 15 IS - 2 SP - 214 EP - 221 AD - Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. siggiae@rockefeller.edu SN - 0959-437X N2 - How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional. KW - cell-models KW - gene-expression KW - genome-transcriptome KW - modeling-and-simulation KW - molecular-signaling KW - physiological-remodeling KW - regulatory-cascades AU - Siggia, ED PY - 2005/04// UR - http://dx.doi.org/10.1016/j.gde.2005.02.004 ER -

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