The Community Simulator: A Python package for microbial ecology

• Published in: PloS one Vol. 15; no. 3; p. e0230430
• Main Authors: Marsland, Robert, Cui, Wenping, Goldford, Joshua, Mehta, Pankaj
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.03.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Community ecology; Computational geometry; Computer and Information Sciences; Computer applications; Computer Simulation; Convexity; Dictionaries; Differential equations; Dynamics; Ecology; Ecology - methods; Ecology and Environmental Sciences; Ecosystems; Environmental conditions; Environmental quality; Equilibrium; Equilibrium states; Integration; Metabolism; Metabolites; Microbial activity; Microbiomes; Microbiota; Microorganisms; Numerical integration; Optimization; Ordinary differential equations; Performance enhancement; Phase transitions; Physical Sciences; Physics; Population; Population biology; Population Dynamics; Pythons; Reason; Research and Analysis Methods; Simulation; Software; United States > US; Massachusetts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0230430

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.