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Optimal Decoding of Cellular Identities in a Genetic Network.

Author(s): Petkova, Mariela; Tkačik, Gašper; Bialek, William; Wieschaus, Eric; Gregor, Thomas

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dc.contributor.authorPetkova, Mariela-
dc.contributor.authorTkačik, Gašper-
dc.contributor.authorBialek, William-
dc.contributor.authorWieschaus, Eric-
dc.contributor.authorGregor, Thomas-
dc.date.accessioned2023-12-12T15:02:46Z-
dc.date.available2023-12-12T15:02:46Z-
dc.date.issued2019-01-31en_US
dc.identifier.citationPetkova, Mariela D, Tkačik, Gašper, Bialek, William, Wieschaus, Eric F, Gregor, Thomas. (2019). Optimal Decoding of Cellular Identities in a Genetic Network. Cell, 176 (4), 844 - 855.e15. doi:10.1016/j.cell.2019.01.007en_US
dc.identifier.issn0092-8674-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1fx73x9c-
dc.description.abstractIn developing organisms, spatially prescribed cell identities are thought to be determined by the expression levels of multiple genes. Quantitative tests of this idea, however, require a theoretical framework capable of exposing the rules and precision of cell specification over developmental time. Using the gap gene network in the early fly embryo as an example, we use such a framework to show how expression levels of the four gap genes can be jointly decoded into an optimal specification of position with 1% accuracy. The decoder correctly predicts, with no free parameters, the dynamics of pair-rule expression patterns at different developmental time points and in various mutant backgrounds. Precise cellular identities are thus available at the earliest stages of development, contrasting the prevailing view of positional information being slowly refined across successive layers of the patterning network. Our results suggest that developmental enhancers closely approximate a mathematically optimal decoding strategy.en_US
dc.format.extent844 - 855en_US
dc.languageengen_US
dc.language.isoen_USen_US
dc.relation.ispartofCellen_US
dc.rightsAuthor's manuscripten_US
dc.titleOptimal Decoding of Cellular Identities in a Genetic Network.en_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1016/j.cell.2019.01.007-
dc.date.eissued2019-01-31en_US
dc.identifier.eissn1097-4172-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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