Simulating Microbial Community Patterning Using Biocellion
Simulating Microbial Community Patterning Using Biocellion
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DOI：
10.1007/978-1-4939-0554-6_16
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发表时间：
2014-01-01
期刊：
影响因子：
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通讯作者：
Momeni, Babak
中科院分区：
文献类型：
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作者：
Kang, Seunghwa;Kahan, Simon;Momeni, Babak
数学建模和计算机模拟是理解细胞及其生物和非生物环境之间复杂相互作用的重要工具：模型行为和观察行为之间的异同为假设形成提供了基础。Momeni等人（Elife 2:e00230, 2013）研究了参与不同类型生态相互作用的酵母菌群落的模式形成，并将数学模型的预测和模拟与湿实验室实验中观察到的实际模式进行了比较。然而，在三维社区中模拟数百万个细胞是非常耗时的。在MATLAB中运行一次模拟可能需要一周或更长时间，从而抑制了对参数组合和假设的广阔空间的探索。提高这种模拟的速度、规模和准确性有助于假设的形成和加速发现。Biocellion是一个高性能的软件框架，用于加速具有数百万到数万亿个细胞的基于离散代理的生物系统模拟。在多核工作站上使用Biocellion只需几个小时，就可以实现与使用MATLAB花费一周计算机时间的模拟相当的规模和精度。Biocellion通过将工作划分到多个计算节点上运行，进一步加速了使用集群计算机的大规模、高分辨率模拟。Biocellion的目标是具有数学建模背景和基本c++编程技能的计算生物学家。本章描述了将最初的Momeni等人的模型作为案例研究调整到bioccelion框架的必要步骤。
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.