Chaste: Cancer, Heart and Soft Tissue Environment

Abstract

Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology.To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology ('Cardiac Chaste'), discrete cell-based modelling of soft tissues ('Cell-based Chaste'), and modelling of ventilation in lungs ('Lung Chaste'). Cardiac Chaste addresses the need for a high-performance, generic, and verified simulation framework for cardiac electrophysiology that is freely available to the scientific community. Cardiac chaste provides a software package capable of realistic heart simulations that is efficient, rigorously tested, and runs on HPC platforms. Cell-based Chaste addresses the need for efficient and verified implementations of cell-based modelling frameworks, providing a set of extensible tools for simulating biological tissues. Computational modelling, along with live imaging techniques, plays an important role in understanding the processes of tissue growth and repair. A wide range of cell-based modelling frameworks have been developed that have each been successfully applied in a range of biological applications. Cell-based Chaste includes implementations of the cellular automaton model, the cellular Potts model, cell-centre models with cell representations as overlapping spheres or Voronoi tessellations, and the vertex model. Lung Chaste addresses the need for a novel, generic and efficient lung modelling software package that is both tested and verified. It aims to couple biophysically-detailed models of airway mechanics with organ-scale ventilation models in a package that is freely available to the scientific community.

Vangl2 regulates the dynamics of Wnt cytonemes in vertebrates

“…On the basis of these findings, we hypothesized that Vangl2 function in the Wnt source cells is crucial for Wnt dissemination via cytonemes which we examined in silico. To quantitatively test the consequences of altered Vangl2 function on gradient formation and tissue patterning in the zebrafish neural plate, we created an agent-based simulation of morphogen distribution via cytonemes using the Chaste modelling software 43,44 . First, we generated a 2D model of the zebrafish gastrula based on the positional information of every cell during the first 10hrs of zebrafish gastrulation 45,46 , representing a portion of the overall gastrula.…”

“…The expansion of the neural plate was modelled in silico via agent-based simulation in the Chaste 43,44 C++ package on a HP Z840 workstation over an Intel Xeon E-series architecture. Forces between cells were defined via a Delaunay-Voronoi triangulation 88,89 .…”

Toward Engineering Biosystems With Emergent Collective Functions

“…Although computational frameworks have been available since the 1980s to support this process, it is only during the past decade that tools have been tailored for synthetic biology applications and reached sufficient performance ( Gorochowski et al, 2012 ; Oishi and Klavins, 2014 ; Goñi-Moreno and Amos, 2015 ). More recently, the effective use of highly parallel computing resources has expanded the complexity of biological models that can be simulated ( Rudge et al, 2012 ; Naylor et al, 2017 ; Li et al, 2019 ; Cooper et al, 2020 ). Automated coarse-graining of representations enable faster simulation without impacting on the accuracy of predictions ( Graham et al, 2017 ), while advanced tools allow verification, validation and uncertainty quantification for such simulations ( Richardson et al, 2020 ).…”

Towards Engineering Biosystems with Emergent Collective Functions

“…Although computational frameworks have been available since the 1980s to support this process, it is only during the past decade that tools have been tailored for synthetic biology applications and reached sufficient performance (Gorochowski et al, 2012;Oishi and Klavins, 2014;Goñi-Moreno and Amos, 2015). More recently, the effective use of highly parallel computing resources has expanded the complexity of biological models that can be simulated (Rudge et al, 2012;Naylor et al, 2017;Li et al, 2019;Cooper et al, 2020). Automated coarse-graining of representations enable faster simulation without impacting on the accuracy of predictions (Graham et al, 2017), while advanced tools allow verification, validation and uncertainty quantification for such simulations (Richardson et al, 2020).…”