Current efforts in cellular disease modeling and regenerative medicine are limited by the paucity of cell types that can be generated in the laboratory. A new study introduces a computational framework, Mogrify, that uses network biology to predict combinations of transcription factors necessary for direct conversion between human cell types to ameliorate this issue.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Rackham, O.J.L. et al. Nat. Genet. 48, 331–335 (2016).
Cahan, P. et al. Cell 158, 903–915 (2014).
D'Alessio, A.C. et al. Stem Cell Rep. 10.1016/j.stemcr.2015.09.016 (23 October 2015).
Marbach, D. et al. Nat. Methods 9, 796–804 (2012).
FANTOM Consortium and the RIKEN PMI and CLST (DGT). Nature 507, 462–470 (2014).
Li, V.C. & Kirschner, M.W. Proc. Natl. Acad. Sci. USA 111, 9503–9508 (2014).
Davis, R.L., Weintraub, H. & Lassar, A.B. Cell 51, 987–1000 (1987).
Morris, S.A. et al. Cell 158, 889–902 (2014).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing financial interests.
Rights and permissions
About this article
Cite this article
Cahan, P. Enabling direct fate conversion with network biology. Nat Genet 48, 226–227 (2016). https://doi.org/10.1038/ng.3516
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.3516
