Towards a Quantitative Understanding of Cell Identity

Research output: Contribution to journalReview article

1 Citation (Scopus)

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 1 2018

Fingerprint

Cell Engineering
Cell Biology
Proteins
Phenotype
Research
Population

Keywords

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

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Review

Cite this

Towards a Quantitative Understanding of Cell Identity. / Ye, Zi; Sarkar, Casim.

In: Trends in Cell Biology, Vol. 28, No. 12, 01.12.2018, p. 1030-1048.

Research output: Contribution to journalReview article

@article{df4c079e4701496da8ba731791ad58fa,
title = "Towards a Quantitative Understanding of Cell Identity",
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.",
keywords = "cell phenotype, cellular decision making, computational modeling, high-throughput data analysis, network biology, phenotypic landscape",
author = "Zi Ye and Casim Sarkar",
year = "2018",
month = "12",
day = "1",
doi = "10.1016/j.tcb.2018.09.002",
language = "English (US)",
volume = "28",
pages = "1030--1048",
journal = "Trends in Cell Biology",
issn = "0962-8924",
publisher = "Elsevier Limited",
number = "12",

}

TY - JOUR

T1 - Towards a Quantitative Understanding of Cell Identity

AU - Ye, Zi

AU - Sarkar, Casim

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

KW - cell phenotype

KW - cellular decision making

KW - computational modeling

KW - high-throughput data analysis

KW - network biology

KW - phenotypic landscape

UR - http://www.scopus.com/inward/record.url?scp=85054460110&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054460110&partnerID=8YFLogxK

U2 - 10.1016/j.tcb.2018.09.002

DO - 10.1016/j.tcb.2018.09.002

M3 - Review article

C2 - 30309735

AN - SCOPUS:85054460110

VL - 28

SP - 1030

EP - 1048

JO - Trends in Cell Biology

JF - Trends in Cell Biology

SN - 0962-8924

IS - 12

ER -