
AbstractModels 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 signalling. 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.
DYNAMICS, 570, Biochemistry & Molecular Biology, 550, Bioinformatics, QH301-705.5, Normal Distribution, Biochemical Research Methods, Epigenesis, Genetic, PATHS, Genetic, Models, SYSTEMS, ROBUSTNESS, Humans, Cell Lineage, Gene Regulatory Networks, Biology (General), 01 Mathematical Sciences, GENE-EXPRESSION, Probability, Stochastic Processes, Science & Technology, Models, Genetic, REGULATORY NETWORK, Gene Expression Profiling, Stem Cells, CIRCUIT, Cell Differentiation, POTENTIAL LANDSCAPE, 06 Biological Sciences, FATE DECISIONS, Gene Expression Regulation, Linear Models, Mathematical & Computational Biology, 08 Information and Computing Sciences, Life Sciences & Biomedicine, MANY-BODY PROBLEM, Algorithms, Epigenesis, Research Article, Signal Transduction
DYNAMICS, 570, Biochemistry & Molecular Biology, 550, Bioinformatics, QH301-705.5, Normal Distribution, Biochemical Research Methods, Epigenesis, Genetic, PATHS, Genetic, Models, SYSTEMS, ROBUSTNESS, Humans, Cell Lineage, Gene Regulatory Networks, Biology (General), 01 Mathematical Sciences, GENE-EXPRESSION, Probability, Stochastic Processes, Science & Technology, Models, Genetic, REGULATORY NETWORK, Gene Expression Profiling, Stem Cells, CIRCUIT, Cell Differentiation, POTENTIAL LANDSCAPE, 06 Biological Sciences, FATE DECISIONS, Gene Expression Regulation, Linear Models, Mathematical & Computational Biology, 08 Information and Computing Sciences, Life Sciences & Biomedicine, MANY-BODY PROBLEM, Algorithms, Epigenesis, Research Article, Signal Transduction
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