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 language | English (US) |
|---|---|
| Pages (from-to) | 1030-1048 |
| Number of pages | 19 |
| Journal | Trends in Cell Biology |
| Volume | 28 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2018 |
Bibliographical note
Publisher Copyright:© 2018 Elsevier Ltd
Keywords
- cell phenotype
- cellular decision making
- computational modeling
- high-throughput data analysis
- network biology
- phenotypic landscape