Monod J. The growth of bacterial cultures. Annu Rev Microbiol. 1949;3:371–94.ArticleÂ
CASÂ
Google ScholarÂ
Hughes SM. plater: read, tidy, and display data from microtiter plates. The J Open Source Softw. 2016;1:106.ArticleÂ
Google ScholarÂ
Wirth NT, Funk J, Donati S, Nikel PI. QurvE: user-friendly software for the analysis of biological growth and fluorescence data. Nat Protoc. 2023;18:2401–3.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Coutin NPJ, Giaever G, Nislow C. Interactively AUDIT your growth curves with a suite of R packages. G3. 2020;10:933–43.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Csibra E, Stan G-B. Parsley: a web app for parsing data from plate readers. Bioinformatics. 2023;39:btad733.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Osthege M, Tenhaef N, Zyla R, Müller C, Hemmerich J, Wiechert W, et al. bletl—a Python package for integrating BioLector microcultivation devices in the design-build-test-learn cycle. Eng Life Sci. 2022;22:242–59.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hall BG, Acar H, Nandipati A, Barlow M. Growth rates made easy. Mol Biol Evol. 2014;31:232–8.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Vaas LAI, Sikorski J, Hofner B, Fiebig A, Buddruhs N, Klenk HP, et al. Opm: An R package for analysing OmniLog® phenotype microarray data. Bioinformatics. 2013;29:1823–4.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Bukhman YV, DiPiazza NW, Piotrowski J, Shao J, Halstead AGW, Bui MD, et al. Modeling microbial growth curves with GCAT. Bioenergy Res. 2015;8:1022–30.ArticleÂ
CASÂ
Google ScholarÂ
Huang L. IPMP 2013—a comprehensive data analysis tool for predictive microbiology. Int J Food Microbiol. 2014;171:100–7.ArticleÂ
PubMedÂ
Google ScholarÂ
Liu Y, Wang X, Liu B, Yuan S, Qin X, Dong Q. Microrisk lab: an online freeware for predictive microbiology. Foodborne Pathog Dis. 2021;18:607–15.ArticleÂ
PubMedÂ
Google ScholarÂ
VerÃssimo A, Paixão L, Neves AR, Vinga S. BGFit: management and automated fitting of biological growth curves. BMC Bioinform. 2013;14:1–6.ArticleÂ
Google ScholarÂ
Vervier K, Browne HP, Lawley TD. CarboLogR: a Shiny/R application for statistical analysis of bacterial utilisation of carbon sources. bioRxiv. 2019;695676.Sprouffske K, Wagner A. Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinform. 2016;17:17–20.ArticleÂ
Google ScholarÂ
Petzoldt T. Growthrates: Estimate Growth Rates from Experimental Data. R package version 0.8.4. 2022. https://CRAN.R-project.org/package=growthratesGarre A, Koomen J, den Besten HMW, Zwietering MH. Modeling Population Growth in R with the biogrowth Package. J Stat Softw. 2023;107(1):1–51.ArticleÂ
Google ScholarÂ
Cuevas DA, Edwards RA. PMAnalyzer: a new web interface for bacterial growth curve analysis. Bioinformatics. 2017;33:1905–6.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cuevas DA, Garza D, Sanchez SE, Rostron J, Henry CS, Vonstein V, et al. Elucidating genomic gaps using phenotypic profiles. F1000Research. 2016;3:1–28.ArticleÂ
Google ScholarÂ
Reiter MA, Vorholt JA. Dashing growth curves: a web application for rapid and interactive analysis of microbial growth curves. BMC Bioinform. 2024;25:67.ArticleÂ
Google ScholarÂ
Swain PS, Stevenson K, Leary A, Montano-Gutierrez LF, Clark IBN, Vogel J, et al. Inferring time derivatives including cell growth rates using Gaussian processes. Nat Commun. 2016;7:13766.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Fernandez-Ricaud L, Kourtchenko O, Zackrisson M, Warringer J, Blomberg A. PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics. BMC Bioinform. 2016;17:249.ArticleÂ
Google ScholarÂ
Jung PP, Christian N, Kay DP, Skupin A, Linster CL. Protocols and programs for high-throughput growth and aging phenotyping in yeast. PLoS ONE. 2015;10: e0119807.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Olsen B, Murakami CJ, Kaeberlein M. YODA: software to facilitate high-throughput analysis of chronological life span, growth rate, and survival in budding yeast. BMC Bioinform. 2010;11:141.ArticleÂ
Google ScholarÂ
Midani FS, Collins J, Britton RA. AMiGA: software for automated analysis of microbial growth assays. Msystems. 2021;6:e00508-e521.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tonner PD, Darnell CL, Bushell FML, Lund PA, Schmid AK, Schmidler SC. A Bayesian non-parametric mixed-effects model of microbial growth curves. PLoS Comput Biol. 2020;16: e1008366.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tonner PD, Darnell CL, Engelhardt BE, Schmid AK. Detecting differential growth of microbial populations with Gaussian process regression. Genome Res. 2017;27:320–33.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hemmerich J, Wiechert W, Oldiges M. Automated growth rate determination in high-throughput microbioreactor systems. BMC Res Notes. 2017;10:617.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Blazanin M, Moore JP, Olsen S, Travisano M. Fight not flight: parasites drive the bacterial evolution of resistance, not avoidance. 2023:2023.04.29.538831.Blazanin M, Vasen E, Jolis CV, An W, Turner P. Theoretical validation of growth curves for quantifying phage-bacteria interactions. bioRxiv. 2023. https://doi.org/10.1101/2023.06.29.546975.R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2022.
Google ScholarÂ
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.BookÂ
Google ScholarÂ
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4:1686.ArticleÂ
Google ScholarÂ
Wickham H, Vaughan D, Girlich M. tidyr: tidy messy data. 2023.Wickham H. Tidy Data. Journal of Statistical Software. 10.Wickham H, François R, Henry L, Müller K, Vaughan D. dplyr: A Grammar of Data Manipulation. R package version 1.1.4. 2023. https://CRAN.R-project.org/package=dplyrSavitzky A, Golay MJ. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 1964;36:1627–39.ArticleÂ
CASÂ
Google ScholarÂ
Cleveland WS, Devlin SJ. Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc. 1988;83:596–610.ArticleÂ
Google ScholarÂ
Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74:829–36.ArticleÂ
Google ScholarÂ
Hastie T, Tibshirani R. Generalized additive models. Stat Sci. 1986;1:297–310.
Google ScholarÂ
Wood SN. Thin-plate regression splines. J R Stat Soc B. 2003;65:95–114.ArticleÂ
Google ScholarÂ
Wood SN. Generalized additive models: an introduction with R. 2nd ed. Chapman and Hall/CRC; 2017. https://doi.org/10.1201/9781315370279.Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc B. 2011;73:3–36.ArticleÂ
Google ScholarÂ
Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.ArticleÂ
Google ScholarÂ
Swinnen IAM, Bernaerts K, Dens EJJ, Geeraerd AH, Van Impe JF. Predictive modelling of the microbial lag phase: a review. Int J Food Microbiol. 2004;94:137–59.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Yates GT, Smotzer T. On the lag phase and initial decline of microbial growth curves. J Theor Biol. 2007;244:511–7.ArticleÂ
PubMedÂ
Google ScholarÂ
Mackie AM, Hassan KA, Paulsen IT, Tetu SG. Biolog phenotype microarrays for phenotypic characterization of microbial cells. In: Paulsen IT, Holmes AJ, editors. Environmental microbiology: methods and protocols. Totowa: Humana Press; 2014. p. 123–30.ChapterÂ
Google ScholarÂ
Biesta-Peters EG, Reij MW, Joosten H, Gorris LGM, Zwietering MH. Comparison of two optical-density-based methods and a plate count method for estimation of growth parameters of Bacillus cereus. Appl Environ Microbiol. 2010;76:1399–405.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ghenu A-H, Marrec L, Bank C. Challenges and pitfalls of inferring microbial growth rates from lab cultures. Front Ecol Evolut. 2024;11:1313500.ArticleÂ
Google ScholarÂ