Towards a Quantitative Understanding of Cell Identity

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Abstract

Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.

Original languageEnglish (US)
Pages (from-to)1030-1048
Number of pages19
JournalTrends in Cell Biology
Volume28
Issue number12
DOIs
StatePublished - Dec 2018

Bibliographical note

Funding Information:
This work was supported by grants from the National Institutes of Health ( R01GM113985 and R01DK114453 ).

Keywords

  • cell phenotype
  • cellular decision making
  • computational modeling
  • high-throughput data analysis
  • network biology
  • phenotypic landscape

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