Open AccessAMiGA: Software for Automated Analysis of Microbial Growth Assays
Author(s) -
Firas S Midani,
James W. Collins,
Robert A. Britton
Publication year - 2021
Publication title -
msystems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.931
H-Index - 39
ISSN - 2379-5077
DOI - 10.1128/msystems.00508-21
Subject(s) - streamlines, streaklines, and pathlines , bacterial growth , exponential growth , biological system , biology , growth rate , growth curve (statistics) , biochemical engineering , statistics , bacteria , mathematics , genetics , mathematical analysis , physics , geometry , engineering , thermodynamics
The analysis of microbial growth is one of the central methods in the field of microbiology. Microbial growth dynamics can be characterized by meaningful parameters, including carrying capacity, exponential growth rate, and growth lag. However, microbial assays with clinical isolates, fastidious organisms, or microbes under stress often produce atypical growth shapes that do not follow the classical microbial growth pattern. Here, we introduce the analysis of microbial growth assays (AMiGA) software, which streamlines the analysis of growth curves without any assumptions about their shapes. AMiGA can pool replicates of growth curves and infer summary statistics for biologically meaningful growth parameters. In addition, AMiGA can quantify death phases and characterize diauxic shifts. It can also statistically test for differential growth under distinct experimental conditions. Altogether, AMiGA streamlines the organization, analysis, and visualization of microbial growth assays. IMPORTANCE Our current understanding of microbial physiology relies on the simple method of measuring microbial populations’ sizes over time and under different conditions. Many advances have increased the throughput of those assays and enabled the study of nonlab-adapted microbes under diverse conditions that widely affect their growth dynamics. Our software provides an all-in-one tool for estimating the growth parameters of microbial cultures and testing for differential growth in a high-throughput and user-friendly fashion without any underlying assumptions about how microbes respond to their growth conditions.