Gcplyr: an R package for microbial growth curve data analysis
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ABSTRACT:
SUBMITTER: Blazanin M
PROVIDER: S-EPMC11232339 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:When we collect the growth curves of many individuals, orderly variation in the curves is often observed rather than a completely random mixture of various curves. Small individuals may exhibit similar growth curves, but the curves differ from those of large individuals, whereby the curves gradually vary from small to large individuals. It has been recognized that after standardization with the asymptotes, if all the growth curves are the same (anamorphic growth curve set), the growth curve sets can be estimated using nonchronological data; otherwise, that is, if the growth curves are not identical after standardization with the asymptotes (polymorphic growth curve set), this estimation is not feasible. However, because a given set of growth curves determines the variation in the observed data, it may be possible to estimate polymorphic growth curve sets using nonchronological data.In this study, we developed an estimation method by deriving the likelihood function for polymorphic growth curve sets. The method involves simple maximum likelihood estimation. The weighted nonlinear regression and least-squares method after the log-transform of the anamorphic growth curve sets were included as special cases.The growth curve sets of the height of cypress (Chamaecyparis obtusa) and larch (Larix kaempferi) trees were estimated. With the model selection process using the AIC and likelihood ratio test, the growth curve set for cypress was found to be polymorphic, whereas that for larch was found to be anamorphic. Improved fitting using the polymorphic model for cypress is due to resolving underdispersion (less dispersion in real data than model prediction).The likelihood function for model estimation depends not only on the distribution type of asymptotes, but the definition of the growth curve set as well. Consideration of these factors may be necessary, even if environmental explanatory variables and random effects are introduced.
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Project description:The ubiquitous heterotrophic marine bacterium, Rugeria pomeroyi, was experimentally cultured under both environmentally realistic carbon conditions and with a tracer-level addition of 13C-labeled leucine. Bacterial protein biosynthesis was tracked through exponential and stationary growth phases. This combination of methods allowed for observation of real-time bacterial protein production of an environmentally relevant marine bacterium under low-carbon conditions to understand metabolic priorities during different growth phases.
2019-07-31 | PXD008661 | Pride
Project description:SummaryBacterial growth curves are essential representations for characterizing bacteria metabolism within a variety of media compositions. Using high-throughput, spectrophotometers capable of processing tens of 96-well plates, quantitative phenotypic information can be easily integrated into the current data structures that describe a bacterial organism. The PMAnalyzer pipeline performs a growth curve analysis to parameterize the unique features occurring within microtiter wells containing specific growth media sources. We have expanded the pipeline capabilities and provide a user-friendly, online implementation of this automated pipeline. PMAnalyzer version 2.0 provides fast automatic growth curve parameter analysis, growth identification and high resolution figures of sample-replicate growth curves and several statistical analyses.Availability and implementationPMAnalyzer v2.0 can be found at https://edwards.sdsu.edu/pmanalyzer/ . Source code for the pipeline can be found on GitHub at https://github.com/dacuevas/PMAnalyzer . Source code for the online implementation can be found on GitHub at https://github.com/dacuevas/PMAnalyzerWeb .Contactdcuevas08@gmail.com.Supplementary informationSupplementary data are available at Bioinformatics online.
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