Welcome to the Berkeley Drosophila Transcription Network Project, a multidisciplinary team at Lawrence Berkeley National Laboratory, the University of California, Berkeley, and the University of California, Davis.

Our goal is to decipher the transcriptional information contained in the extensive cis-acting DNA sequences that direct the patterns of gene expression that underlie animal development. Using the early embryo of the fruitfly Drosophila melanogaster as a model, we are developing experimental and computational methods to systematically characterize and dissect the complex expression patterns and regulatory interactions already present prior to gastrulation. We have identified 37 principal regulatory factors within this network for initial analysis together with their target genes.

Components of this effort include:
3D gene expression: we are developing advanced image analysis methods to capture the expression patterns of hundreds of genes in 3D at cellular resolution
In vivo DNA binding: we are using in vivo crosslinking and Affymetrix whole genome tiling arrays to measure binding of endogenous factors to DNA elements in living embryos
In vitro DNA binding: we are measuring the in vitro DNA binding specificities of transcription factors active in the early embryo using purified, over-expressed proteins and a modified binding site selection (SELEX) protocol
Transgenic promoter analysis: we are testing and characterizing the ability of a large number of predicted potential cis regulatory sequences to drive reporter-gene expression in the early embryo
Automation/engineering: we are building devices to automate our data collection methods, including an automated Drosophila embryo injector and a fly handling robot
Bioinformatic modeling: we are developing methods to utilize the above data and Drosophila genome sequences to model crucial aspects of the early transcriptional network and to decode the cis-regulatory information in non-coding DNA.
This project is chiefly funded by a grant from NIGMS and NHGRI, R01 GM070444. Additional funding comes from grants to Michael Eisen, Sue Celniker, and Bernd Hamann.