MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A stochastic model dissects cell states in biological transition processes

Author(s)
Armond, Jonathan W.; Saha, Krishanu; Rana, Anas A.; Oates, Chris J.; Jaenisch, Rudolf; Nicodemi, Mario; Mukherjee, Sach; ... Show more Show less
Thumbnail
DownloadArmond-2014-A stochastic model.pdf (1.127Mb)
PUBLISHER_CC

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0
Metadata
Show full item record
Abstract
Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.
Date issued
2014-01
URI
http://hdl.handle.net/1721.1/88212
Department
Massachusetts Institute of Technology. Department of Biology; Whitehead Institute for Biomedical Research
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Armond, Jonathan W., Krishanu Saha, Anas A. Rana, Chris J. Oates, Rudolf Jaenisch, Mario Nicodemi, and Sach Mukherjee. “A Stochastic Model Dissects Cell States in Biological Transition Processes.” Sci. Rep. 4 (January 17, 2014).
Version: Final published version
ISSN
2045-2322

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.