Keywords: population growth, nonlinear models, differential equation
Webpages:
https://CRAN.R-project.org/package=growthrates,
https://github.com/tpetzoldt/growthrates The population growth rate is a direct measure of fitness, common in many disciplines of theoretical and applied biology, e.g. physiology, ecology, eco-toxicology or pharmacology. The
R package
growthrates aims to streamline growth rate estimation from direct or indirect measures of population density (e.g. cell counts, optical density or fluorescence) of batch experiments or field observations. It can be applicable to different species of bacteria, protists, and metazoa, e.g.
E. coli,
Cyanobacteria,
Paramecium, green algae or
Daphnia.
The package includes three types of methods:
- Fitting of linear models to the period of exponential growth using the “growth rates made easy”-method of Hall and Barlow (2013),
- Nonparametric growthrate estimation by using smoothers. The current implementation uses function smooth.spline, similar to method of package grofit (Kahm et al. 2010),
- Nonlinear fitting of parametric models like logistic, Gompertz, Baranyi or Huang (Huang 2011) is done with package FME (Flexible Modelling Environment) of Soetaert and Petzoldt (2010). Growth models can be given either in closed form or as numerically integrated system of differential equations, that are numerically solved with package deSolve (Soetaert, Petzoldt, and Setzer 2010) and cOde (Kaschek 2016).
The package contains methods to fit single data sets or complete experimental series. It uses S4 classes and contains functions for extracting results (e.g. coef, summary, residuals, …), and methods for convenient plotting. The fits and the growth models can be visualized with
shiny apps.
References Hall, Acar, B. G., and M. Barlow. 2013. “Growth Rates Made Easy.”
Mol. Biol. Evol. 31: 232–38. doi:
10.1093/molbev/mst197.
Huang, Lihan. 2011. “A New Mechanistic Growth Model for Simultaneous Determination of Lag Phase Duration and Exponential Growth Rate and a New Belehdredek-Type Model for Evaluating the Effect of Temperature on Growth Rate.”
Food Microbiology 28 (4): 770–76. doi:
10.1016/j.fm.2010.05.019.
Kahm, Matthias, Guido Hasenbrink, Hella Lichtenberg-Frate, Jost Ludwig, and Maik Kschischo. 2010. “Grofit: Fitting Biological Growth Curves with R.”
Journal of Statistical Software 33 (7): 1–21. doi:
10.18637/jss.v033.i07.
Kaschek, Daniel. 2016.
cOde: Automated C Code Generation for Use with the deSolve and bvpSolve Packages.
https://CRAN.R-project.org/package=cOde.
Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.”
Journal of Statistical Software 33 (3): 1–28. doi:
10.18637/jss.v033.i03.
Soetaert, Karline, Thomas Petzoldt, and R. Woodrow Setzer. 2010. “Solving Differential Equations in R: Package deSolve.”
Journal of Statistical Software 33 (9): 1–25. doi:
10.18637/jss.v033.i09.
