Transition state characteristics during cell differentiation
File(s)
Published version
Author(s)
Brackston, Rowan
Lakatos, Eszter
Stumpf, MPH
Type
Journal Article
Abstract
Models describing the process of stem-cell differentiation are plentiful, and may offer insights into the underlying mechanisms and experimentally observed behaviour. Waddington’s epigenetic landscape has been providing a conceptual framework for differentiation processes since its inception. It also allows, however, for detailed mathematical and quantitative analyses, as the landscape can, at least in principle, be related to mathematical models of dynamical systems. Here we focus on a set of dynamical systems features that are intimately linked to cell differentiation, by considering exemplar dynamical models that capture important aspects of stem cell differentiation dynamics. These models allow us to map the paths that cells take through gene expression space as they move from one fate to another, e.g. from a stem-cell to a more specialized cell type. Our analysis highlights the role of the transition state (TS) that separates distinct cell fates, and how the nature of the TS changes as the underlying landscape changes—change that can be induced by e.g. cellular signaling. We demonstrate that models for stem cell differentiation may be interpreted in terms of either a static or transitory landscape. For the static case the TS represents a particular transcriptional profile that all cells approach during differentiation. Alternatively, the TS may refer to the commonly observed period of heterogeneity as cells undergo stochastic transitions.
Date Issued
2018-09-20
Date Acceptance
2018-07-27
Citation
PLoS Computational Biology, 2018, 14 (9), pp.1-24
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Start Page
1
End Page
24
Journal / Book Title
PLoS Computational Biology
Volume
14
Issue
9
Copyright Statement
© 2018 Brackston et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor
Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Council (BBSRC)
Identifier
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006405
Grant Number
BB/N003608/1
BB/G020434/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
MANY-BODY PROBLEM
POTENTIAL LANDSCAPE
GENE-EXPRESSION
REGULATORY NETWORK
FATE DECISIONS
DYNAMICS
SYSTEMS
PATHS
ROBUSTNESS
CIRCUIT
Algorithms
Cell Differentiation
Cell Lineage
Epigenesis, Genetic
Gene Expression Profiling
Gene Expression Regulation
Gene Regulatory Networks
Humans
Linear Models
Models, Genetic
Normal Distribution
Probability
Signal Transduction
Stem Cells
Stochastic Processes
Stem Cells
Humans
Linear Models
Probability
Normal Distribution
Stochastic Processes
Gene Expression Profiling
Signal Transduction
Cell Differentiation
Gene Expression Regulation
Epigenesis, Genetic
Cell Lineage
Algorithms
Models, Genetic
Gene Regulatory Networks
Bioinformatics
06 Biological Sciences
08 Information and Computing Sciences
01 Mathematical Sciences
Publication Status
Published
Date Publish Online
2018-09-20