Computational Identification of Transcription Networks in Embryonic Stem Cells
Award year: 2008-2009
Embryonic Stem Cells (ESCs) possess several notable properties that account for their exceptional scientific and medical importance, including development of treatments to degenerative, malignant, or genetic diseases such as diabetes, Parkinson's disease, Alzheimer's and heart failure, as well as injury due to inflammation, infection, and trauma, such as spinal cord injury. Transcriptional control is thought to be a key control mechanism for ESCs to maintain their undifferentiated state. We propose to study the engineering principles built within the transcriptional networks of human and mouse ESCs, and to use reverse-engineering approaches to reconstruct these transcriptional networks. Through biophysical modeling, computational inference and biological validation, the blueprints of the transcription networks of ESCs will be revealed. This work will advance Network Biology in ESCs with regard to self-renewal, pluripotency, cell-type specific differentiation and evolutionary constraints. The biophysical modeling and computational tools will be standardized and assembled into a publicly available software pipeline for guiding the manipulation of stem cells for cell-based therapies.