社区模拟器：微生物生态学的Python软件包
The Community Simulator: A Python package for microbial ecology
摘要：
天然微生物群落包含数百至数千种相互作用的物种。因此，计算机模拟在微生物生态学中发挥着越来越重要的作用。在这篇手稿中，我们提出了一个新的开源，免费提供的Python包称为社区模拟器，用于以可重复，透明和可扩展的方式模拟微生物种群动力学。社区模拟器包括五个大量元素：准备一组样本的初始状态和环境条件的工具，基于建模假设字典的动态方程的自动生成，代谢和分类结构的可调水平的随机参数采样，动态方程的并行集成，以及支持样本之间的迁移的亚稳态动态。为了使用Community Simulator显著加快模拟速度，我们的Python包实现了一种新的期望最大化（EM）算法，用于寻找社区动态的平衡状态，该算法利用了最近发现的生态动态和凸优化之间的对偶性。我们目前的数据表明，这种EM算法提高了性能之间的一个和两个订单相比，直接数值积分相应的常微分方程。最后，我们列出了几个最近的应用程序的社区模拟器的微生物生态学的问题，并讨论了可能的扩展包直接分析微生物组成数据。
英文摘要：
Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
