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Predicting expression patterns from regulatory sequence in Drosophila segmentation


The establishment of complex expression patterns at precise times and locations is key to metazoan development, yet a mechanistic understanding of the underlying transcription control networks is still missing. Here we describe a novel thermodynamic model that computes expression patterns as a function of cis-regulatory sequence and of the binding-site preferences and expression of participating transcription factors. We apply this model to the segmentation gene network of Drosophila melanogaster and find that it predicts expression patterns of cis-regulatory modules with remarkable accuracy, demonstrating that positional information is encoded in the regulatory sequence and input factor distribution. Our analysis reveals that both strong and weaker binding sites contribute, leading to high occupancy of the module DNA, and conferring robustness against mutation; short-range homotypic clustering of weaker sites facilitates cooperative binding, which is necessary to sharpen the patterns. Our computational framework is generally applicable to most protein–DNA interaction systems.

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Figure 1: Overview of the thermodynamic model and approach.
Figure 2: Predicted expression patterns and model validation.
Figure 3: Participating transcription factors and their behaviour.
Figure 4: Cooperative DNA binding and binding site overlap.
Figure 5: Regulatory input and expression of segmentation modules.


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We thank D. Leaman and M. Dandapani for the in vivo analysis of binding sites and are indebted to E. Siggia, S. Sinha and J. Widom for valuable discussions at the outset of the project. This work was supported by a Fellowship from the Center for Studies in Physics and Biology at Rockefeller University (E.S.), by the European Network of Excellence (E.S. and T.R.-S.), by a Rockefeller University Graduate Fellowship (M.S.) and by an NIH grant (U.G.); E.S. is the incumbent of the Soretta and Henry Shapiro career development chair.

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Correspondence to Eran Segal or Ulrike Gaul.

Supplementary information

Supplementary Information

The file contains Supplementary Figures 1-12 with Legends and Supplementary Methods. The Supplementary Figures include additional experimental and computational results. The Supplementary Methods provide a detailed description of the computational model. (PDF 1202 kb)

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Segal, E., Raveh-Sadka, T., Schroeder, M. et al. Predicting expression patterns from regulatory sequence in Drosophila segmentation. Nature 451, 535–540 (2008).

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