Stochastic Phenotype Transition of a Single Cell in an Intermediate Region of Gene State Switching

Ge, Hao; Qian, Hong; Xie, X. Sunney

doi:10.1103/physrevlett.114.078101

Cited by 84 publications

(74 citation statements)

References 53 publications

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“…[36][37][38][39][40][41][42][43] Most of these schemes still, however, rely on there being a sufficient degree of time scale separation between gene and protein degrees of freedom, which means that switching dynamics must usually be assumed to be very fast. While such an assumption could be a sound one for many gene circuits, the intermediate regime where there is no such time scale separation is also of interest especially for understanding the behavior of mammalian gene circuits.…”

Section: Introductionmentioning

confidence: 99%

Dichotomous noise models of gene switches

Potoyan

Wolynes

2015

The Journal of Chemical Physics

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Molecular noise in gene regulatory networks has two intrinsic components, one part being due to fluctuations caused by the birth and death of protein or mRNA molecules which are often present in small numbers and the other part arising from gene state switching, a single molecule event. Stochastic dynamics of gene regulatory circuits appears to be largely responsible for bifurcations into a set of multi-attractor states that encode different cell phenotypes. The interplay of dichotomous single molecule gene noise with the nonlinear architecture of genetic networks generates rich and complex phenomena. In this paper, we elaborate on an approximate framework that leads to simple hybrid multi-scale schemes well suited for the quantitative exploration of the steady state properties of large-scale cellular genetic circuits. Through a path sum based analysis of trajectory statistics, we elucidate the connection of these hybrid schemes to the underlying master equation and provide a rigorous justification for using dichotomous noise based models to study genetic networks. Numerical simulations of circuit models reveal that the contribution of the genetic noise of single molecule origin to the total noise is significant for a wide range of kinetic regimes. C 2015 AIP Publishing LLC. [http://dx

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Section: Introductionmentioning

confidence: 99%

Dichotomous noise models of gene switches

Potoyan

Wolynes

2015

The Journal of Chemical Physics

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“…A number of methods exist for determining the decomposition in which the potential is defined; however, by far the most common is via the steady state probability distribution over the state space, where U ∝ − ln(P s (X)). Numerous studies have obtained landscapes based upon this approach, either using methods that directly obtain the steady state distribution (Lv et al, 2014;Ge et al, 2015;, or use extensive simulations to estimate the distribution empirically (Wang et al, 2008(Wang et al, , 2010aLi et al, 2011;. Alternative approaches include those based upon variational principles and the action in moving between any two points (Wang et al, 2010bLv et al, 2014), empirical Lyapunov functions (Bhattacharya et al, 2011) or an orthogonal decomposition of the vector field (Zhou et al, 2012).…”

Section: Limitations To Gradient Based Dynamicsmentioning

confidence: 99%

Transition State Characteristics During Cell Differentiation

Brackston

Lakatos

Stumpf

2018

Preprint

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M odels 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.

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“…Such cellular processes 36 involving dedifferentiation and cell-fate switching might constitute a fundamental 37 element of a tissue's capacity to self-repair and rejuvenate [5,6]. However, such 38 physiological (normal) cell reprogramming might have pathological consequences if the 39 acquisition of epigenetic and phenotypic plasticity is not transient. In response to 40 chronically permissive tissue environments for in vivo reprogramming, the occurrence of 41 unrestrained epigenetic plasticity might permanently lock cells into self-renewing (a) (b) Fig 2. Schematic reprentation of the ER-GRN model and its multiscale reduction.…”

Section: Introduction 25mentioning

confidence: 99%

“…Our deconstruction of epigenetic plasticity and phenotypic malleability 730 provides crucial insights into how pathological states of permanently acquired 731 pluripotency can be therapeutically unlocked by exploiting epigenetic heterogeneity. 732 We have added an ER layer to previous approaches in which cell phenotypes were 733 associated with the attractors of complex gene regulatory systems and their robustness, 734 with the resilience of such attractors tuned by the presence of intrinsic noise, 735 environmental fluctuations, and other disturbances [35][36][37][38][39][40][41][42][43]. Our approach is based on 736 two main pillars: namely, a framework for the generation of the ensemble of ER systems, 737 and a multiscale asymptotic analysis-based method for model reduction of the stochastic 738 ER-GRN model (see Section Multi-scale analysis and model reduction).…”

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confidence: 99%

A multiscale model of epigenetic heterogeneity reveals the kinetic routes of pathological cell fate reprogramming

Folguera-Blasco

Pérez-Carrasco

Cuyàs

et al. 2018

Preprint

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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. Furthermore, based on the existence of multiple scales, we formulate a method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme that allows us to deal with such complex systems. 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. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We then formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (pluripotent stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validated 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 from pathological to physiological might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer. Author summaryCertain modifications of the structure and functioning of the protein/DNA complex 1 called chromatin can allow adult, fully differentiated cells to adopt a stem cell-like 2 pluripotent state in a purely epigenetic manner, not involving changes in the underlying 3 DNA sequence. Such reprogramming-like phenomena may constitute an innate 4 reparative route through which human tissues respond to injury and could also serve as 5 a novel regenerative strategy in human pathological situations in which tissue or organ 6 repair is impaired. However, it should be noted that in vivo reprogramming would be 7 capable of maintaining tissue homeostasis provided the acquisition of pluripotency 8 features is strictly transient and accompanied by an accurate replenishment of the 9 specific cell types being lost. Crucially, an excessive reprogramming to pluripotency in 10 the absence of controlled re-differentiation would impair the repair or the replacement of 11 damaged cells, thereby promoting pathological alterations of cell fate. A mechanistic 12 understanding of how the degree of chromatin plasticity dictates the reparative versus 13 pathological behaviour of in vivo reprogramming to rejuvenate aged tissues while 14 preventing tumorigenesis is urgently needed, including especially th...

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Stochastic Phenotype Transition of a Single Cell in an Intermediate Region of Gene State Switching

Cited by 84 publications

References 53 publications

Dichotomous noise models of gene switches

Dichotomous noise models of gene switches

Transition State Characteristics During Cell Differentiation

A multiscale model of epigenetic heterogeneity reveals the kinetic routes of pathological cell fate reprogramming

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