ArticlePLoS computational biology2019
A multiscale model of epigenetic heterogeneity-driven cell fate decision-making.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 33 citations in OpenAlex.
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- Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity.Bulletin of mathematical biology · 2021Article
- Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs.PLoS computational biology · 2021Article
- A mathematical model exhibiting the effect of DNA methylation on the stability boundary in cell-fate networks.Epigenetics · 2021Article
- A computer-guided design tool to increase the efficiency of cellular conversions.Nature communications · 2021Article
- Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch.Molecular systems biology · 2020Article
- Metformin: Sentinel of the Epigenetic Landscapes That Underlie Cell Fate and Identity.Biomolecules · 2020Review
- Mesoscale Liquid Model of Chromatin Recapitulates Nuclear Order of Eukaryotes.Biophysical journal · 2020Article
- Article
- Modeling binary and graded cone cell fate patterning in the mouse retina.PLoS computational biology · 2020Article
- A computational model of stem cell molecular mechanism to maintain tissue homeostasis.PloS one · 2020Article
- Quantifying the Stability of Coupled Genetic and Epigenetic Switches With Variational Methods.Frontiers in genetics · 2020Article
- Intracellular Energy Variability Modulates Cellular Decision-Making Capacity.Scientific reports · 2019Article
- Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inherent capacity of somatic cells to switch their phenotypic status in response to damage stimuli in vivo might have a pivotal role in ageing and cancer. However, how the entry-exit mechanisms of phenotype reprogramming are established remains poorly understood. In an attempt to elucidate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-gene regulatory network (GRN) to study the plastic phenotypic behaviours driven by ER heterogeneity. To deal with such complex system, we additionally formulate a multiscale asymptotic method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme. Our analysis of the coupled system reveals a regime of tristability in which pluripotent stem-like and differentiated steady-states coexist with a third indecisive state, with ER driving transitions between these states. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validates the likelihood of such switching involving solely kinetic changes in epigenetic factors. Our results suggest that epigenetic heterogeneity regulates the mechanisms and kinetics of phenotypic robustness of cell fate reprogramming. The occurrence of tunable switches capable of modifying the nature of cell fate reprogramming might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer.
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