Recent advances in genomics technologies have provided the means to efficiently generate several types of global data that are informative on gene regulatory systems in human and mouse. In this project we will develop the methods in experimental design and computational analysis that are necessary for the successful use of these technologies in the study of complex gene regulatory networks in these species.
Aim 1 : Computational inference of gene regulatory network. We will combine the power of targeted gene perturbations, global functional genomic measurements, and computational inference and modeling, to develop an integrated, predictive approach for the study of gene regulatory networks in mammals. Although the combined analysis of gene expression data and transcription factor binding location data have been useful for the inference of gene regulatory network in simple organisms, such analyses have so far met with limited success in mammals. We will identify the key factors impeding progress and will design ways to ameliorate them. We will develop a "structure oriented" approach for the inference of a gene regulatory network from diverse data types, including data on the responses to gene perturbations. Our goal is to design and demonstrate an effective approach to infer complex gene regulatory networks responsible for maintaining stable cellular states in mammalian cells.
Aim 2 : Implementation our method to study gene regulatory systems in ESC The approach developed in aim 1 will be applied to study the gene regulatory network underlying pluripotency and self-renewal in mouse embryonic stem cells (ESC). We have previously shown that in this important cell type a major portion of the variation in global gene expression (65%) can be predicted by the binding patterns of a moderate number (12) of transcription factors. We will generate global gene expression data at selected time points after targeted gene perturbations on these and other regulators. Based on these data sets, together with the large amount of regulator binding location data already available for unperturbed ESC, we will use the methods developed in aim 1 to infer the gene regulatory network responsible for the maintenance of the undifferentiated state.

Public Health Relevance

In this project we will develop the methods in experimental design and computational analysis that are necessary for the successful use of high throughput genomics technologies in the study of complex gene regulatory networks in mammals. Aim 1: Computational inference of gene regulatory network We will combine the power of targeted gene perturbations, global functional genomic measurements, and computational inference and modeling, to develop an integrated, predictive approach for the study of gene regulatory networks in mammals. Although the combined analysis of gene expression data and transcription factor binding location data have been useful for the inference of gene regulatory network in simple organisms, such analyses have so far met with limited success in mammals. We will identify the key factors impeding progress and will design ways to ameliorate them. We will develop a structure oriented approach for the inference of a gene regulatory network from diverse data types, including data on the responses to gene perturbations. Our goal is to design and demonstrate an effective approach to infer complex gene regulatory networks responsible for maintaining stable cellular states in mammalian cells. Aim 2: Implementation our method to study gene regulatory systems in ESC The approach developed in aim 1 will be applied to study the gene regulatory network underlying pluripotency and self-renewal in mouse embryonic stem cells (ESC). We have previously shown that in this important cell type a major portion of the variation in global gene expression (65%) can be predicted by the binding patterns of a moderate number (12) of transcription factors. We will generate global gene expression data at selected time points after targeted gene perturbations on these and other regulators. Based on these data sets, together with the large amount of regulator binding location data already available for unperturbed ESC, we will use the methods developed in aim 1 to infer the gene regulatory network responsible for the maintenance of the undifferentiated state.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG006018-02
Application #
8320163
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Pazin, Michael J
Project Start
2011-08-17
Project End
2014-05-31
Budget Start
2012-06-01
Budget End
2013-05-31
Support Year
2
Fiscal Year
2012
Total Cost
$380,052
Indirect Cost
$130,052
Name
Stanford University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Ma, Xin; Xiao, Luo; Wong, Wing Hung (2014) Learning regulatory programs by threshold SVD regression. Proc Natl Acad Sci U S A 111:15675-80
Brady, Jennifer J; Li, Mavis; Suthram, Silpa et al. (2013) Early role for IL-6 signalling during generation of induced pluripotent stem cells revealed by heterokaryon RNA-Seq. Nat Cell Biol 15:1244-52