Literature DB >> 7748785

Mechanism of eve stripe formation.

J Reinitz1, D H Sharp.   

Abstract

In this paper we analyze the formation of stripes of expression of the pair-rule gene eve. We identify detailed mechanisms which control the formation of stripes 2-5. Each stripe is formed as a result of generalized activation by bcd and ubiquitous transcription factors combined with localized repression by gap genes. Each of the eight stripe borders of these four stripes is shown to be under the control of a particular gap gene expression domain. Protein synthesis from eve and its controlling gap genes begins at the same time, but localized eve expression is substantially delayed relative to localized expression of gap domains. We show that this delay results from a change in the spatial balance between activation and repression due to the intensification and refinement of gap domains during cleavage cycle 14. eve stripe formation is ordered in time; stripe 2 appears earlier than stripes 3-5. We show that this happens because the formation of stripe 2 is less dependent on gap domain refinement than is the case for stripes 3-5: Each of stripes 3-5 is controlled by a pair of overlapping gap domains, whereas stripe 2 is controlled by a disjoint pair of gap domains. Finally, we observe that eve stripes do not form unless Eve protein has an extremely small diffusivity, and argue that this low diffusivity is a result of the apical localization of pair-rule message. This implies that localization of pair-rule message is required for stripe formation. The essential tool used to obtain these results is the method of gene circuits, which is a new approach to the analysis of gene expression data. Its purpose is to provide a way to use this data to infer how concentrations of products of a given gene change with time and how these changes are influenced by the activating or repressing effects of the products of other genes. The gene circuit method is based on three main ideas, explained in the paper. First is the choice of protein concentrations as state variables for the description of gene regulation. Second is the summary of chemical reaction kinetics by coarse-grained rate equations for protein concentrations. Third is the use of least squares fits to gene expression data to measure phenomenological parameters occurring in the gene circuit.

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Mesh:

Year:  1995        PMID: 7748785     DOI: 10.1016/0925-4773(94)00310-j

Source DB:  PubMed          Journal:  Mech Dev        ISSN: 0925-4773            Impact factor:   1.882


  70 in total

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Authors:  E Torigoi; I M Bennani-Baiti; C Rosen; K Gonzalez; P Morcillo; M Ptashne; D Dorsett
Journal:  Proc Natl Acad Sci U S A       Date:  2000-03-14       Impact factor: 11.205

2.  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.

Authors:  Daniel E Zak; Gregory E Gonye; James S Schwaber; Francis J Doyle
Journal:  Genome Res       Date:  2003-11       Impact factor: 9.043

3.  Dynamical analysis of regulatory interactions in the gap gene system of Drosophila melanogaster.

Authors:  Johannes Jaeger; Maxim Blagov; David Kosman; Konstantin N Kozlov; Ekaterina Myasnikova; Svetlana Surkova; Carlos E Vanario-Alonso; Maria Samsonova; David H Sharp; John Reinitz
Journal:  Genetics       Date:  2004-08       Impact factor: 4.562

4.  The genetics of geometry.

Authors:  Enrico Coen; Anne-Gaëlle Rolland-Lagan; Mark Matthews; J Andrew Bangham; Przemyslaw Prusinkiewicz
Journal:  Proc Natl Acad Sci U S A       Date:  2004-02-11       Impact factor: 11.205

5.  Quantitatively predictable control of Drosophila transcriptional enhancers in vivo with engineered transcription factors.

Authors:  Justin Crocker; Garth R Ilsley; David L Stern
Journal:  Nat Genet       Date:  2016-02-08       Impact factor: 38.330

6.  A sequence level model of an intact locus predicts the location and function of nonadditive enhancers.

Authors:  Kenneth A Barr; John Reinitz
Journal:  PLoS One       Date:  2017-07-17       Impact factor: 3.240

7.  Characterization of the Drosophila segment determination morphome.

Authors:  Svetlana Surkova; David Kosman; Konstantin Kozlov; Ekaterina Myasnikova; Anastasia A Samsonova; Alexander Spirov; Carlos E Vanario-Alonso; Maria Samsonova; John Reinitz
Journal:  Dev Biol       Date:  2007-11-04       Impact factor: 3.582

8.  A generative bias towards average complexity in artificial cell lineages.

Authors:  Rolf Lohaus; Nicholas L Geard; Janet Wiles; Ricardo B R Azevedo
Journal:  Proc Biol Sci       Date:  2007-07-22       Impact factor: 5.349

9.  Multifunctionality and robustness trade-offs in model genetic circuits.

Authors:  Olivier C Martin; Andreas Wagner
Journal:  Biophys J       Date:  2008-04-15       Impact factor: 4.033

10.  Mathematical model of the Drosophila circadian clock: loop regulation and transcriptional integration.

Authors:  Hassan M Fathallah-Shaykh; Jerry L Bona; Sebastian Kadener
Journal:  Biophys J       Date:  2009-11-04       Impact factor: 4.033

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