Organisms devote a significant fraction of their genomes to encoding regulatory information which specifies when and where different genes should be turned on or off. Such information is essential for understanding development, tissue specificity, and cellular response to the environment and has great importance for understanding the molecular basis of disease. The one dimensional regulatory programs encoded in DNA are executed by transcription factors which respond to different conditions and regulate gene expression combinatorially, leading to complex regulatory networks. The goal of the proposed research is to develop theoretical models and computational algorithms to decipher the regulatory programs and to reconstruct the transcription networks in the model organism Saccharomyces cerevisiae. The following are the specific aims: 1) to systematically map the DNA recognition sites and target genes of transcription factors in the genome, 2) to identify environmental and genetic perturbations under which each transcription factor is activated or deactivated, 3) to systematically analyze combinatorial regulation by multiple transcription factors, 4) collaborate with two experimental labs to address specific problems of transcriptional regulation and combinatorial control, and 5) to build databases and online tools for the biological community to analyze gene expression data and transcriptional regulation. We will develop methods integrating information from the genome sequences for several yeast species and genome-wide functional data, and we will combine bioinformatics analysis with mechanistic models of gene regulation.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM070808-03
Application #
7031547
Study Section
Genome Study Section (GNM)
Program Officer
Tompkins, Laurie
Project Start
2004-04-01
Project End
2008-03-31
Budget Start
2006-04-01
Budget End
2007-03-31
Support Year
3
Fiscal Year
2006
Total Cost
$261,699
Indirect Cost
Name
University of California San Francisco
Department
Biochemistry
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Xie, Zhengwei; Jay, Kyle A; Smith, Dana L et al. (2015) Early telomerase inactivation accelerates aging independently of telomere length. Cell 160:928-939
Zuleta, Ignacio A; Aranda-Díaz, Andrés; Li, Hao et al. (2014) Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry. Nat Methods 11:443-8
Nelson, Christopher S; Fuller, Chris K; Fordyce, Polly M et al. (2013) Microfluidic affinity and ChIP-seq analyses converge on a conserved FOXP2-binding motif in chimp and human, which enables the detection of evolutionarily novel targets. Nucleic Acids Res 41:5991-6004
He, Xin; Fuller, Chris K; Song, Yi et al. (2013) Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am J Hum Genet 92:667-80
Huang, Vera; Zheng, Jiashun; Qi, Zhongxia et al. (2013) Ago1 Interacts with RNA polymerase II and binds to the promoters of actively transcribed genes in human cancer cells. PLoS Genet 9:e1003821
Xie, Zhengwei; Zhang, Yi; Zou, Ke et al. (2012) Molecular phenotyping of aging in single yeast cells using a novel microfluidic device. Aging Cell 11:599-606
Rafelski, Susanne M; Viana, Matheus P; Zhang, Yi et al. (2012) Mitochondrial network size scaling in budding yeast. Science 338:822-4
Chubukov, Victor; Zuleta, Ignacio A; Li, Hao (2012) Regulatory architecture determines optimal regulation of gene expression in metabolic pathways. Proc Natl Acad Sci U S A 109:5127-32
Zhang, Yi; Luo, Chunxiong; Zou, Ke et al. (2012) Single cell analysis of yeast replicative aging using a new generation of microfluidic device. PLoS One 7:e48275
Zheng, Jiashun; Benschop, Joris J; Shales, Michael et al. (2010) Epistatic relationships reveal the functional organization of yeast transcription factors. Mol Syst Biol 6:420

Showing the most recent 10 out of 24 publications