The role of mechanical interactions in EMT

Murphy, Ryan J; Buenzli, Pascal R.; Tambyah, Tamara A.; Thompson, Erik W.; Hugo, Honor J.; Baker, Ruth E.; Simpson, Matthew J.

doi:10.1088/1478-3975/abf425

Cited by 9 publications

(9 citation statements)

References 65 publications

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“…Subsequently, Kang et al [50] modeled, with a landscape approach, a 16-node metabolism-EMT-metastasis network that integrates metabolism circuit [48], EMT core circuit [15,51,52], and metastasis circuit [53]. Some recent mathematical models focused on mechanical interactions to understand the gene regulation of cells losing cellular cohesion during EMT [54][55][56].…”

Section: Emt Circuits Coupled With Other Processesmentioning

confidence: 99%

Computational systems‐biology approaches for modeling gene networks driving epithelial–mesenchymal transitions

Katebi

Ramirez

2021

Comp Sys Onco

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Epithelial–mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell–cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.

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Section: Emt Circuits Coupled With Other Processesmentioning

confidence: 99%

Computational systems‐biology approaches for modeling gene networks driving epithelial–mesenchymal transitions

Katebi

Ramirez

2021

Comp Sys Onco

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“…Alternatively, at a tissue scale, a hybrid model could be developed where cells are modelled as discrete entities and their behaviour is related to the underlying extracellular matrix, or other nutrients, whose density may be described using a continuum model (Wang et al, 2019). A different approach is to convert the existing discrete model to a continuum model, that can then be related to wound healing processes in the deeper layers of skin (Tambyah et al, 2020;Murphy et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Push or Pull? Cell Proliferation and Migration During Wound Healing

2022

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Wound healing of the skin is a complex process that is still not well-understood. Wound management is expensive for both individuals and the health system overall, and can reduce quality of life for patients. Given these significant socio-economic impacts, wound healing has long been a focus of scientific research. Recent in vivo mouse studies have identified two key regions in wounded skin tissue: A non-proliferative leading edge that actively migrates into wounded space, and a proliferative hub in which cells have enhanced mitotic properties. This work uses mathematical and computational modelling to investigate the effect of changing the mechanical characteristics of cells in these two key regions. In this paper we explore what characteristics are sufficient for wound healing, particularly focusing on cell proliferation, since wounds are not able to repair successfully without sufficient levels of cell division. By considering contact inhibited proliferation, where small cells are unable to divide, we find that a quiescent region develops if the proliferative hub is able to grow over time, essentially limiting the number of cells that are able to divide. In contrast, if the size of the proliferative hub is kept below some threshold, then contact inhibition has a less significant role in wound repair. This work builds upon existing cell-based computational studies of wound healing and could be modified to investigate different stages of wound healing, impaired healing and wound treatments.

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“…Mathematical models of population dynamics are often constructed by considering both discrete and continuous descriptions, allowing for both microscopic and macroscopic details to be considered [1]. This approach has been applied to several kinds of discrete models, including cellular Potts models [2][3][4][5], exclusion processes [6][7][8][9], mechanical models of epithelial tissues [10][11][12][13][14][15][16][17], hydrodynamics [18,19] and a variety of other types of individual-based models [1,[20][21][22][23][24][25][26][27]. Continuum models are useful for describing collective behaviour, especially because the computational requirement of discrete models increases with the size of the population, and this can become computationally prohibitive for large populations, which is particularly problematic for parameter inference [28].…”

Section: Introductionmentioning

confidence: 99%

“…One challenge with using coarse-grained continuum limit models is that while the solution of these models can match averaged data from the corresponding discrete model for certain choices of parameters [10,17,31], the solution of the continuous model can be a very poor approximation for other parameter choices [13,15,32,33]. More generally, coarse-grained models are typically only valid in certain asymptotic parameter regimes [31,33,34].…”

Section: Introductionmentioning

confidence: 99%

“…They are important in a variety of contexts, such as wound healing [47,48] and cancer [49,50]. Many models have been developed for studying their dynamics, considering both discrete and continuum modelling [10][11][12][13][14][15][16], with most models given in the form of a nonlinear reaction-diffusion equation with a moving boundary, using a nonlinear diffusivity term to incorporate mechanical relaxation and a source term to model cell proliferation [12,13,16]. These continuum limit models too are only accurate in certain parameter regimes, becoming inaccurate if the rate of mechanical relaxation is slow relative to the rate of proliferation [13,15,33].…”

Section: Introductionmentioning

confidence: 99%

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Pushing coarse-grained models beyond the continuum limit using equation learning

VandenHeuvel,

Buenzli,

Simpson

2024

Proc. R. Soc. A.

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Mathematical modelling of biological population dynamics often involves proposing high-fidelity discrete agent-based models that capture stochasticity and individual-level processes. These models are often considered in conjunction with an approximate coarse-grained differential equation that captures population-level features only. These coarse-grained models are only accurate in certain asymptotic parameter regimes, such as enforcing that the time scale of individual motility far exceeds the time scale of birth/death processes. When these coarse-grained models are accurate, the discrete model still abides by conservation laws at the microscopic level, which implies that there is some macroscopic conservation law that can describe the macroscopic dynamics. In this work, we introduce an equation learning framework to find accurate coarse-grained models when standard continuum limit approaches are inaccurate. We demonstrate our approach using a discrete mechanical model of epithelial tissues, considering a series of four case studies that consider problems with and without free boundaries, and with and without proliferation, illustrating how we can learn macroscopic equations describing mechanical relaxation, cell proliferation, and the equation governing the dynamics of the free boundary of the tissue. While our presentation focuses on this biological application, our approach is more broadly applicable across a range of scenarios where discrete models are approximated by approximate continuum-limit descriptions.

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The role of mechanical interactions in EMT

Cited by 9 publications

References 65 publications

Computational systems‐biology approaches for modeling gene networks driving epithelial–mesenchymal transitions

Computational systems‐biology approaches for modeling gene networks driving epithelial–mesenchymal transitions

Push or Pull? Cell Proliferation and Migration During Wound Healing

Pushing coarse-grained models beyond the continuum limit using equation learning

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