Title |
Epigenetic Landscapes Explain Partially Reprogrammed Cells and Identify Key Reprogramming Genes
|
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Published in |
PLoS Computational Biology, August 2014
|
DOI | 10.1371/journal.pcbi.1003734 |
Pubmed ID | |
Authors |
Alex H. Lang, Hu Li, James J. Collins, Pankaj Mehta |
Abstract |
A common metaphor for describing development is a rugged "epigenetic landscape" where cell fates are represented as attracting valleys resulting from a complex regulatory network. Here, we introduce a framework for explicitly constructing epigenetic landscapes that combines genomic data with techniques from spin-glass physics. Each cell fate is a dynamic attractor, yet cells can change fate in response to external signals. Our model suggests that partially reprogrammed cells are a natural consequence of high-dimensional landscapes, and predicts that partially reprogrammed cells should be hybrids that co-express genes from multiple cell fates. We verify this prediction by reanalyzing existing datasets. Our model reproduces known reprogramming protocols and identifies candidate transcription factors for reprogramming to novel cell fates, suggesting epigenetic landscapes are a powerful paradigm for understanding cellular identity. |
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Germany | 1 | 6% |
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Demographic breakdown
Type | Count | As % |
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Scientists | 5 | 29% |
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Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 4 | 2% |
Vietnam | 1 | <1% |
Netherlands | 1 | <1% |
Spain | 1 | <1% |
Finland | 1 | <1% |
Unknown | 208 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 73 | 34% |
Researcher | 42 | 19% |
Student > Bachelor | 20 | 9% |
Student > Master | 13 | 6% |
Professor > Associate Professor | 12 | 6% |
Other | 28 | 13% |
Unknown | 28 | 13% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 64 | 30% |
Biochemistry, Genetics and Molecular Biology | 42 | 19% |
Physics and Astronomy | 35 | 16% |
Engineering | 10 | 5% |
Medicine and Dentistry | 10 | 5% |
Other | 24 | 11% |
Unknown | 31 | 14% |