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      Transition state characteristics during cell differentiation

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          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.

          Author summary

          Current emphasis on single cell analysis, especially in the context of the human and mouse cell atlas projects, is on characterizing the transcriptomic signatures of different cell states. This is clearly of great importance, as even the number of different cell types, e.g. in humans, is not known with any satisfying degree of certainty. There are enormous challenges in mapping these states, but this will still only provide a partial answer. Importantly, the way in which cells differentiate, and the way in which gene expression changes over the course of differentiation will still be unknown. Here we use a dynamical systems perspective to consider the nature of, and dynamics during, the transition between different cell types (or cell states). We show how the developmental landscape (in Waddington’s sense) and the nature of the transition states change in response to external stimuli and discuss this in the context of stem cell differentiation (as well as its potential reversal). In particular, we discuss how the nature of the landscape at the transition state, as well as the presence of non-gradient dynamics, has strong implications for the identifiability of differentiation dynamics from experimental data.

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          Stochastic resonance

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            Epigenetic modulators, modifiers and mediators in cancer aetiology and progression.

            This year is the tenth anniversary of the publication in this journal of a model suggesting the existence of 'tumour progenitor genes'. These genes are epigenetically disrupted at the earliest stages of malignancies, even before mutations, and thus cause altered differentiation throughout tumour evolution. The past decade of discovery in cancer epigenetics has revealed a number of similarities between cancer genes and stem cell reprogramming genes, widespread mutations in epigenetic regulators, and the part played by chromatin structure in cellular plasticity in both development and cancer. In the light of these discoveries, we suggest here a framework for cancer epigenetics involving three types of genes: 'epigenetic mediators', corresponding to the tumour progenitor genes suggested earlier; 'epigenetic modifiers' of the mediators, which are frequently mutated in cancer; and 'epigenetic modulators' upstream of the modifiers, which are responsive to changes in the cellular environment and often linked to the nuclear architecture. We suggest that this classification is helpful in framing new diagnostic and therapeutic approaches to cancer.
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              The chemical Langevin equation

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                Author and article information

                Contributors
                Role: Formal analysisRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                September 2018
                20 September 2018
                : 14
                : 9
                : e1006405
                Affiliations
                [1 ] Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom
                [2 ] School of BioScience and School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
                Oxford, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.

                Author information
                http://orcid.org/0000-0003-3103-4345
                http://orcid.org/0000-0002-3577-1222
                Article
                PCOMPBIOL-D-18-00633
                10.1371/journal.pcbi.1006405
                6168170
                30235202
                519f90a0-2e9a-41c2-bf9c-a8b3b8fca630
                © 2018 Brackston et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 April 2018
                : 27 July 2018
                Page count
                Figures: 9, Tables: 0, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/G020434/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000761, Imperial College London;
                Award ID: Schrödinger Fellowship
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/G020434/1
                Award Recipient :
                EL gratefully acknowledges support through a Schrödinger Fellowship from Imperial College London. RDB and MPHS are funded from the Biotechnology and Biological Sciences Research Council (BBSRC) through grant BB/G020434/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Developmental Biology
                Cell Differentiation
                Physical Sciences
                Chemistry
                Physical Chemistry
                Reaction Dynamics
                Transition State
                Computer and Information Sciences
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Systems Science
                Dynamical Systems
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Stem Cells
                Physical Sciences
                Mathematics
                Algebra
                Linear Algebra
                Eigenvalues
                Biology and Life Sciences
                Genetics
                Epigenetics
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Biology and Life Sciences
                Genetics
                Gene Expression
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-10-02
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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