Simulating Microbial Community Patterning Using Biocellion
Mathematical modeling and computer simulation are important tools for understanding complex interactions between cells and their biotic and abiotic environment: similarities and differences between modeled and observed behavior provide the basis for hypothesis formation. Momeni et al. (Elife 2:e00230, 2013) investigated pattern formation in communities of yeast strains engaging in different types of ecological interactions, comparing the predictions of mathematical modeling, and simulation to actual patterns observed in wet-lab experiments. However, simulations of millions of cells in a three-dimensional community are extremely time consuming. One simulation run in MATLAB may take a week or longer, inhibiting exploration of the vast space of parameter combinations and assumptions. Improving the speed, scale, and accuracy of such simulations facilitates hypothesis formation and expedites discovery. Biocellion is a high-performance software framework for accelerating discrete agent-based simulation of biological systems with millions to trillions of cells. Simulations of comparable scale and accuracy to those taking a week of computer time using MATLAB require just hours using Biocellion on a multicore workstation. Biocellion further accelerates large scale, high resolution simulations using cluster computers by partitioning the work to run on multiple compute nodes. Biocellion targets computational biologists who have mathematical modeling backgrounds and basic C++ programming skills. This chapter describes the necessary steps to adapt the original Momeni et al.’s model to the Biocellion framework as a case study.
Key wordsDiscrete agent-based modeling Partial differential equation Adaptive mesh refinement High-performance computing Cell system simulation
Support for this research was provided by the Extreme Scale Computing Initiative and the Fundamental and Computational Sciences Directorate, as part of the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory (PNNL). Portions of this work were conducted using PNNL Institutional Computing at PNNL. PNNL is operated by Battelle for DOE under contract DE-ACO5-76RLO 1830. B.M. is a Gordon and Betty Moore Foundation fellow of the Life Sciences Research Foundation.
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