Inference, validation, and dynamic modeling of transcription networks in multipotent hematopoietic cells.
Identifying the transcription factor interactions that are responsible for cell-specific gene expression programs is key to understanding the regulation of cell behaviors, such as self-renewal, proliferation, differentiation, and death. The rapidly increasing availability of microarray-derived global gene expression data sets, coupled with genome sequence information from multiple species, has driven the development of computational methods to reverse engineer and dynamically model genetic regulatory networks. An understanding of the architecture and behavior of transcriptional networks should lend insight into how the huge number of potential gene expression programs is constrained and facilitates efforts to direct or redirect cell fate.
Soneji
Shamit
S
Weatherall Institute of Molecular Medicine, Molecular Haematology Unit, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK.
Huang
Sui
S
Loose
Matthew
M
Donaldson
Ian John
IJ
Patient
Roger
R
Göttgens
Berthold
B
Enver
Tariq
T
May
Gillian
G
D000818:Animals; D002454:Cell Differentiation; D019070:Cell Lineage; D019295:Computational Biology; D003198:Computer Simulation; D050982:GATA1 Transcription Factor / Q000378:metabolism; D053263:Gene Regulatory Networks; D006412:Hematopoietic Stem Cells / Q000166:cytology*; D006413:Hematopoietic System; D006801:Humans; D008954:Models, Biological; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D014158:Transcription, Genetic
D050982:GATA1 Transcription Factor; C494688:GATA1 protein, human