647 research outputs found
Particle-based simulation of ellipse-shaped particle aggregation as a model for vascular network formation
Computational modelling is helpful for elucidating the cellular mechanisms
driving biological morphogenesis. Previous simulation studies of blood vessel
growth based on the Cellular Potts model (CPM) proposed that elongated,
adhesive or mutually attractive endothelial cells suffice for the formation of
blood vessel sprouts and vascular networks. Because each mathematical
representation of a model introduces potential artifacts, it is important that
model results are reproduced using alternative modelling paradigms. Here, we
present a lattice-free, particle-based simulation of the cell elongation model
of vasculogenesis. The new, particle-based simulations confirm the results
obtained from the previous Cellular Potts simulations. Furthermore, our current
findings suggest that the emergence of order is possible with the application
of a high enough attractive force or, alternatively, a longer attraction
radius. The methodology will be applicable to a range of problems in
morphogenesis and noisy particle aggregation in which cell shape is a key
determining factor.Comment: 9 pages, 11 figures, 2 supplementary videos (on Youtube), submitted
to Computational Particle Mechanics, special issue: Jos\'e-Manuel Garcia
Aznar (Ed.) Particle-based simulations on cell and biomolecular mechanic
Implementing vertex dynamics models of cell populations in biology within a consistent computational framework
The dynamic behaviour of epithelial cell sheets plays a central role during development, growth, disease and wound healing. These processes occur as a result of cell adhesion, migration, division, differentiation and death, and involve multiple processes acting at the cellular and molecular level. Computational models offer a useful means by which to investigate and test hypotheses about these processes, and have played a key role in the study of cell–cell interactions. However, the necessarily complex nature of such models means that it is difficult to make accurate comparison between different models, since it is often impossible to distinguish between differences in behaviour that are due to the underlying model assumptions, and those due to differences in the in silico implementation of the model. In this work, an approach is described for the implementation of vertex dynamics models, a discrete approach that represents each cell by a polygon (or polyhedron) whose vertices may move in response to forces. The implementation is undertaken in a consistent manner within a single open source computational framework, Chaste, which comprises fully tested, industrial-grade software that has been developed using an agile approach. This framework allows one to easily change assumptions regarding force generation and cell rearrangement processes within these models. The versatility and generality of this framework is illustrated using a number of biological examples. In each case we provide full details of all technical aspects of our model implementations, and in some cases provide extensions to make the models more generally applicable
A parallelized cellular Potts model that enables simulations at tissue scale
The Cellular Potts Model (CPM) is a widely used simulation paradigm for
systems of interacting cells that has been used to study scenarios ranging from
plant development to morphogenesis, tumour growth and cell migration. Despite
their wide use, CPM simulations are considered too computationally intensive
for three-dimensional (3D) models at organ scale. CPMs have been difficult to
parallelise because of their inherently sequential update scheme. Here, we
present a Graphical Processing Unit (GPU)-based parallelisation scheme that
preserves local update statistics and is up to 3-4 orders of magnitude faster
than serial implementations. We show several examples where our scheme
preserves simulation behaviors that are drastically altered by existing
parallelisation methods. We use our framework to construct tissue-scale models
of liver and lymph node environments containing millions of cells that are
directly based on microscopy-imaged tissue structures. Thus, our GPU-based CPM
framework enables in silico studies of multicellular systems of unprecedented
scale.Comment: 29 pages, 11 figures, 3 table
Virtual cardiac monolayers for electrical wave propagation
The complex structure of cardiac tissue is considered to be one of the main determinants of an arrhythmogenic substrate. This study is aimed at developing the first mathematical model to describe the formation of cardiac tissue, using a joint in silico-in vitro approach. First, we performed experiments under various conditions to carefully characterise the morphology of cardiac tissue in a culture of neonatal rat ventricular cells. We considered two cell types, namely, cardiomyocytes and fibroblasts. Next, we proposed a mathematical model, based on the Glazier-Graner-Hogeweg model, which is widely used in tissue growth studies. The resultant tissue morphology was coupled to the detailed electrophysiological Korhonen-Majumder model for neonatal rat ventricular cardiomyocytes, in order to study wave propagation. The simulated waves had the same anisotropy ratio and wavefront complexity as those in the experiment. Thus, we conclude that our approach allows us to reproduce the morphological and physiological properties of cardiac tissue
A node-based version of the cellular Potts model
The cellular Potts model (CPM) is a lattice-based Monte Carlo method that uses an energetic formalism to describe the phenomenological mechanisms underlying the biophysical problem of interest. We here propose a CPM-derived framework that relies on a node-based representation of cell-scale elements. This feature has relevant consequences on the overall simulation environment. First, our model can be implemented on any given domain, provided a proper discretization (which can be regular or irregular, fixed or time evolving). Then, it allowed an explicit representation of cell membranes, whose displacements realistically result in cell movement. Finally, our node-based approach can be easily interfaced with continuous mechanics or fluid dynamics models. The proposed computational environment is here applied to some simple biological phenomena, such as cell sorting and chemotactic migration, also in order to achieve an analysis of the performance of the underlying algorithm. This work is finally equipped with a critical comparison between the advantages and disadvantages of our model with respect to the traditional CPM and to some similar vertex-based approaches
Multiscale developments of cellular Potts models
Multiscale problems are ubiquitous and fundamental in all biological phenomena that emerge naturally from the complex interaction of processes which occur at various levels. A number of both discrete and continuous mathematical models and methods have been developed to address such an intricate network of organization. One of the most suitable individual cell-based model for this purpose is the well-known cellular Potts model (CPM). The CPM is a discrete, lattice-based, flexible technique that is able to accurately identify and describe the phenomenological mechanisms which are responsible for innumerable biological (and nonbiological) phenomena. In this work, we first give a brief overview of its biophysical basis and discuss its main limitations. We then propose some innovative extensions, focusing on ways of integrating the basic mesoscopic CPM with accurate continuous models of microscopic dynamics of individuals. The aim is to create a multiscale hybrid framework that is able to deal with the typical multilevel organization of biological development, where the behavior of the simulated individuals is realistically driven by their internal state. Our CPM extensions are then tested with sample applications that show a qualitative and quantitative agreement with experimental data. Finally, we conclude by discussing further possible developments of the metho
Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression
Angiogenesis involves the formation of new blood vessels by sprouting or
splitting of existing blood vessels. During sprouting, a highly motile type of
endothelial cell, called the tip cell, migrates from the blood vessels followed
by stalk cells, an endothelial cell type that forms the body of the sprout. To
get more insight into how tip cells contribute to angiogenesis, we extended an
existing computational model of vascular network formation based on the
cellular Potts model with tip and stalk differentiation, without making a
priori assumptions about the differences between tip cells and stalk cells. To
predict potential differences, we looked for parameter values that make tip
cells (a) move to the sprout tip, and (b) change the morphology of the
angiogenic networks. The screening predicted that if tip cells respond less
effectively to an endothelial chemoattractant than stalk cells, they move to
the tips of the sprouts, which impacts the morphology of the networks. A
comparison of this model prediction with genes expressed differentially in tip
and stalk cells revealed that the endothelial chemoattractant Apelin and its
receptor APJ may match the model prediction. To test the model prediction we
inhibited Apelin signaling in our model and in an \emph{in vitro} model of
angiogenic sprouting, and found that in both cases inhibition of Apelin or of
its receptor APJ reduces sprouting. Based on the prediction of the
computational model, we propose that the differential expression of Apelin and
APJ yields a "self-generated" gradient mechanisms that accelerates the
extension of the sprout.Comment: 48 pages, 10 figures, 8 supplementary figures. Accepted for
publication in PLoS ON
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