A GPU Algorithm for Agent-Based Models to Simulate the Integration of Cell Membrane Signals

Simulation of complex biological systems with agent-based models is becoming more relevant with the increase in Graphics Processing Unit (GPU) power. In those simulations, up to millions of virtual cells are individually computed, involving daunting processing times. An important part of computational models is the algorithm that manages how agents perceive their surroundings. This can be particularly problematic in three-dimensional environments where agents have deformable virtual membranes. This article presents a GPU algorithm that gives the possibility for agents to integrate the signals scattered on their virtual membrane. It is detailed to be coded in languages like OpenCL or Cuda. Its performances are tested to show its speed with current GPU devices. Finally, it was implemented inside an existing software to test and illustrate the possibilities it offers.

[1]  Vincent Rodin,et al.  A Software Architecture for Multi-Cellular System Simulations on Graphics Processing Units , 2013, Acta biotheoretica.

[2]  P. Ballet SimCells , an advanced software for multicellular modeling Application to tumoral and blood vessel co-development , 2018 .

[3]  Nicholas S. Flann,et al.  Biocellion: accelerating computer simulation of multicellular biological system models , 2014, Bioinform..

[4]  Hojung Nam,et al.  DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences , 2018, PLoS Comput. Biol..

[5]  James Sharpe,et al.  ya||a: GPU-Powered Spheroid Models for Mesenchyme and Epithelium. , 2019, Cell systems.

[6]  J. Sharpe Computer modeling in developmental biology: growing today, essential tomorrow , 2017, Development.

[7]  Daniela M. Romano,et al.  High performance cellular level agent-based simulation with FLAME for the GPU , 2010, Briefings Bioinform..

[8]  Abbas Shirinifard,et al.  Multi-scale modeling of tissues using CompuCell3D. , 2012, Methods in cell biology.

[9]  Luigi Preziosi,et al.  Cell Mechanics. From single scale-based models to multiscale modeling , 2010 .

[10]  Alexander R. A. Anderson,et al.  Single-Cell-Based Models in Biology and Medicine , 2007 .

[11]  Emmanuel Barillot,et al.  PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling , 2018, bioRxiv.

[12]  Eberhard O Voit,et al.  Agent-based modeling of morphogenetic systems: Advantages and challenges , 2019, PLoS Comput. Biol..