Stem Cell Fate Decision Making: Modeling Approaches

Alexander A. Spector, Warren L Grayson

Research output: Contribution to journalReview article

Abstract

Mathematical (computational) modeling approaches can be effective tools in providing insight into cell-fate decisions. In this article, several major approaches to the modeling of embryonic, hematopoietic, adipose-derived, cancer, and neural stem cell differentiation are discussed. First, the population dynamics approach is considered. The models described as bifurcating dynamical systems that result in bistability or periodic oscillations are then discussed. Also, spatiotemporal models of cell differentiation, including continuum and discrete (agent- and rule-based) approaches, are reviewed. Further, the effects of the mechanical factors are discussed, including the convergence of the differentiation and mechanotransducton pathways and computational analysis of the extracellular matrix (surrounding tissue). Finally, the stochastic models that take into account the molecular noise of internal and external origins are reviewed. The effectiveness of the modeling in the creation of the improved differentiation platforms, elucidation of various pathological conditions, and analysis of treatment regiments has been demonstrated.

Original languageEnglish (US)
Pages (from-to)2702-2711
Number of pages10
JournalACS Biomaterials Science and Engineering
Volume3
Issue number11
DOIs
StatePublished - Nov 13 2017

Fingerprint

Stem cells
Decision making
Population dynamics
Stochastic models
Dynamical systems
Tissue

Keywords

  • cell differentiation
  • dynamical system
  • mechanobiology
  • stochastic methods

ASJC Scopus subject areas

  • Biomaterials
  • Biomedical Engineering

Cite this

Stem Cell Fate Decision Making : Modeling Approaches. / Spector, Alexander A.; Grayson, Warren L.

In: ACS Biomaterials Science and Engineering, Vol. 3, No. 11, 13.11.2017, p. 2702-2711.

Research output: Contribution to journalReview article

@article{edfc3d8b3cdc47c89fe6d81d4f896cfc,
title = "Stem Cell Fate Decision Making: Modeling Approaches",
abstract = "Mathematical (computational) modeling approaches can be effective tools in providing insight into cell-fate decisions. In this article, several major approaches to the modeling of embryonic, hematopoietic, adipose-derived, cancer, and neural stem cell differentiation are discussed. First, the population dynamics approach is considered. The models described as bifurcating dynamical systems that result in bistability or periodic oscillations are then discussed. Also, spatiotemporal models of cell differentiation, including continuum and discrete (agent- and rule-based) approaches, are reviewed. Further, the effects of the mechanical factors are discussed, including the convergence of the differentiation and mechanotransducton pathways and computational analysis of the extracellular matrix (surrounding tissue). Finally, the stochastic models that take into account the molecular noise of internal and external origins are reviewed. The effectiveness of the modeling in the creation of the improved differentiation platforms, elucidation of various pathological conditions, and analysis of treatment regiments has been demonstrated.",
keywords = "cell differentiation, dynamical system, mechanobiology, stochastic methods",
author = "Spector, {Alexander A.} and Grayson, {Warren L}",
year = "2017",
month = "11",
day = "13",
doi = "10.1021/acsbiomaterials.6b00606",
language = "English (US)",
volume = "3",
pages = "2702--2711",
journal = "ACS Biomaterial Science and Engineering",
issn = "2373-9878",
publisher = "American Chemical Society",
number = "11",

}

TY - JOUR

T1 - Stem Cell Fate Decision Making

T2 - Modeling Approaches

AU - Spector, Alexander A.

AU - Grayson, Warren L

PY - 2017/11/13

Y1 - 2017/11/13

N2 - Mathematical (computational) modeling approaches can be effective tools in providing insight into cell-fate decisions. In this article, several major approaches to the modeling of embryonic, hematopoietic, adipose-derived, cancer, and neural stem cell differentiation are discussed. First, the population dynamics approach is considered. The models described as bifurcating dynamical systems that result in bistability or periodic oscillations are then discussed. Also, spatiotemporal models of cell differentiation, including continuum and discrete (agent- and rule-based) approaches, are reviewed. Further, the effects of the mechanical factors are discussed, including the convergence of the differentiation and mechanotransducton pathways and computational analysis of the extracellular matrix (surrounding tissue). Finally, the stochastic models that take into account the molecular noise of internal and external origins are reviewed. The effectiveness of the modeling in the creation of the improved differentiation platforms, elucidation of various pathological conditions, and analysis of treatment regiments has been demonstrated.

AB - Mathematical (computational) modeling approaches can be effective tools in providing insight into cell-fate decisions. In this article, several major approaches to the modeling of embryonic, hematopoietic, adipose-derived, cancer, and neural stem cell differentiation are discussed. First, the population dynamics approach is considered. The models described as bifurcating dynamical systems that result in bistability or periodic oscillations are then discussed. Also, spatiotemporal models of cell differentiation, including continuum and discrete (agent- and rule-based) approaches, are reviewed. Further, the effects of the mechanical factors are discussed, including the convergence of the differentiation and mechanotransducton pathways and computational analysis of the extracellular matrix (surrounding tissue). Finally, the stochastic models that take into account the molecular noise of internal and external origins are reviewed. The effectiveness of the modeling in the creation of the improved differentiation platforms, elucidation of various pathological conditions, and analysis of treatment regiments has been demonstrated.

KW - cell differentiation

KW - dynamical system

KW - mechanobiology

KW - stochastic methods

UR - http://www.scopus.com/inward/record.url?scp=85031900763&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85031900763&partnerID=8YFLogxK

U2 - 10.1021/acsbiomaterials.6b00606

DO - 10.1021/acsbiomaterials.6b00606

M3 - Review article

AN - SCOPUS:85031900763

VL - 3

SP - 2702

EP - 2711

JO - ACS Biomaterial Science and Engineering

JF - ACS Biomaterial Science and Engineering

SN - 2373-9878

IS - 11

ER -