ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium

Germann, Philipp; Marín-Riera, Miquel; Sharpe, James

doi:10.1016/j.cels.2019.02.007

Cited by 39 publications

(35 citation statements)

References 53 publications

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“…Agent-based models can also be developed using other programming languages, such as Python or R, but C++ is generally preferred as it allows a simple creation and organization of classes to describe the system components, providing efficient memory management and good performance. Commonly used frameworks include CGAL (Fabri et al, 2000), CellSys (Hoehme and Drasdo, 2010), CHASTE (Pitt-Francis et al, 2009; Mirams et al, 2013), CompuCell3D (Swat et al, 2012), MecaGen (Delile et al, 2014), EmbryoMaker (Marin-Riera et al, 2016), PhysiCell (Ghaffarizadeh et al, 2018), PhisiBoSS (Letort et al, 2018), and ya||a (Germann et al, 2019) (see Table 1 (II), for source codes).…”

Section: Discussionmentioning

confidence: 99%

Mathematical Models of Organoid Cultures

2019

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Organoids are engineered three-dimensional tissue cultures derived from stem cells and capable of self-renewal and self-organization into a variety of progenitors and differentiated cell types. An organoid resembles the cellular structure of an organ and retains some of its functionality, while still being amenable to in vitro experimental study. Compared with two-dimensional cultures, the three-dimensional structure of organoids provides a more realistic environment and structural organization of in vivo organs. Similarly, organoids are better suited to reproduce signaling pathway dynamics in vitro, due to a more realistic physiological environment. As such, organoids are a valuable tool to explore the dynamics of organogenesis and offer routes to personalized preclinical trials of cancer progression, invasion, and drug response. Complementary to experiments, mathematical and computational models are valuable instruments in the description of spatiotemporal dynamics of organoids. Simulations of mathematical models allow the study of multiscale dynamics of organoids, at both the intracellular and intercellular levels. Mathematical models also enable us to understand the underlying mechanisms responsible for phenotypic variation and the response to external stimulation in a cost- and time-effective manner. Many recent studies have developed laboratory protocols to grow organoids resembling different organs such as the intestine, brain, liver, pancreas, and mammary glands. However, the development of mathematical models specific to organoids remains comparatively underdeveloped. Here, we review the mathematical and computational approaches proposed so far to describe and predict organoid dynamics, reporting the simulation frameworks used and the models’ strengths and limitations.

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Section: Discussionmentioning

confidence: 99%

Mathematical Models of Organoid Cultures

2019

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“…More generally, in several other systems, the skin physical forces exerted on/by cells by/on the extra-cellular environment (e.g., shear stress, compression, tension, traction, adhesion) have been linked to changes in extra-cellular matrix architecture, cell cycle, cell motility and signalling [61][62][63][64][65][66], likely playing a defining role in patterning processes. With the advent of biophysical tools to measure physical parameters in vivo [67] and the ever growing amount of theoretical frameworks integrating biomechanics [68][69][70][71], it now becomes possible to explore the role of tissue mechanics with comprehensive experimental modelling approaches. One may infer previous unified models by adding explicit dependence of some parameters of reaction-diffusion and chemotaxis terms on mechanical parameters (e.g., molecular diffusion could be a function of substrate stiffness [72]).…”

Section: Designing Numerical Evo-devo Approaches To Study Tissue Mechmentioning

confidence: 99%

A “Numerical Evo-Devo” Synthesis for the Identification of Pattern-Forming Factors

Bailleul

Manceau

Touboul

2020

Cells

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Animals display extensive diversity in motifs adorning their coat, yet these patterns have reproducible orientation and periodicity within species or groups. Morphological variation has been traditionally used to dissect the genetic basis of evolutionary change, while pattern conservation and stability in both mathematical and organismal models has served to identify core developmental events. Two patterning theories, namely instruction and self-organisation, emerged from this work. Combined, they provide an appealing explanation for how natural patterns form and evolve, but in vivo factors underlying these mechanisms remain elusive. By bridging developmental biology and mathematics, novel frameworks recently allowed breakthroughs in our understanding of pattern establishment, unveiling how patterning strategies combine in space and time, or the importance of tissue morphogenesis in generating positional information. Adding results from surveys of natural variation to these empirical-modelling dialogues improves model inference, analysis, and in vivo testing. In this evo-devo-numerical synthesis, mathematical models have to reproduce not only given stable patterns but also the dynamics of their emergence, and the extent of inter-species variation in these dynamics through minimal parameter change. This integrative approach can help in disentangling molecular, cellular and mechanical interaction during pattern establishment.

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“…In silico Organoid. The "organoid" dataset comes from a simulation of morphogenesis using the ya||a software (20). The organoid is the product of a simulation of branching morphogenesis with epithelium and mesenchyme (only the epithelial cells are recorded in this example).…”

Section: R a F Tmentioning

confidence: 99%

CeLaVi: An Interactive Cell Lineage Visualisation Tool

Salvador-Martínez

Grillo

Averof

et al. 2020

Preprint

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Recent innovations in genetics and imaging are providing the means to reconstruct cell lineages, either by tracking cell divisions using live microscopy, or by deducing the history of cells using molecular recorders. A cell lineage on its own, however, is simply a description of cell divisions as branching events. A major goal of current research is to integrate this description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. Visualising, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualisation tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualising the spatial distribution, identities and properties of cells. CeLaVi's principal functions include the ability to explore and manipulate the cell lineage tree; to visualise the spatial distribution of cell clones at different depths of the tree; to colour cells in the 3D viewer based on lineage relationships; to visualise various cell qualities on the 3D viewer (e.g. gene expression, cell type, tissue layer) and to annotate selected cells/clones. All these capabilities are demonstrated with four different example data sets. CeLaVi is available at http://www.celavi.pro.

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ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium

Cited by 39 publications

References 53 publications

Mathematical Models of Organoid Cultures

Mathematical Models of Organoid Cultures

A “Numerical Evo-Devo” Synthesis for the Identification of Pattern-Forming Factors

CeLaVi: An Interactive Cell Lineage Visualisation Tool

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