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grofit: Fitting Biological Growth Curves with R

Author

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  • Kahm, Matthias
  • Hasenbrink, Guido
  • Lichtenberg-Fraté, Hella
  • Ludwig, Jost
  • Kschischo, Maik

Abstract

The grofit package was developed to fit many growth curves obtained under different conditions in order to derive a conclusive dose-response curve, for instance for a compound that potentially affects growth. grofit fits data to different parametric models and in addition provides a model free spline method to circumvent systematic errors that might occur within application of parametric methods. This amendment increases the reliability of the characteristic parameters (e.g.,lag phase, maximal growth rate, stationary phase) derived from a single growth curve. By relating obtained parameters to the respective condition (e.g.,concentration of a compound) a dose response curve can be derived that enables the calculation of descriptive pharma-/toxicological values like half maximum effective concentration (EC50). Bootstrap and cross-validation techniques are used for estimating confidence intervals of all derived parameters.

Suggested Citation

  • Kahm, Matthias & Hasenbrink, Guido & Lichtenberg-Fraté, Hella & Ludwig, Jost & Kschischo, Maik, 2010. "grofit: Fitting Biological Growth Curves with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i07).
    • Handle: RePEc:jss:jstsof:v:033:i07
    DOI: http://hdl.handle.net/10.18637/jss.v033.i07
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    Cited by:

    1. Lea A I Vaas & Johannes Sikorski & Victoria Michael & Markus Göker & Hans-Peter Klenk, 2012. "Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-18, April.
    2. Ernest C Y Ho & Juan Nicolas Malagón & Abha Ahuja & Rama Singh & Ellen Larsen, 2018. "Rotation of sex combs in Drosophila melanogaster requires precise and coordinated spatio-temporal dynamics from forces generated by epithelial cells," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-28, October.
    3. Christian Ritz & Florent Baty & Jens C Streibig & Daniel Gerhard, 2015. "Dose-Response Analysis Using R," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-13, December.
    4. Román-Román, P. & Torres-Ruiz, F., 2015. "A stochastic model related to the Richards-type growth curve. Estimation by means of simulated annealing and variable neighborhood search," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 579-598.
    5. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    6. Yu Gan & Zifeng Lu & Hao Cai & Michael Wang & Xin He & Steven Przesmitzki, 2020. "Future private car stock in China: current growth pattern and effects of car sales restriction," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(3), pages 289-306, March.
    7. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
    8. Bonnell, Tyler R. & Ghai, Ria R. & Goldberg, Tony L. & Sengupta, Raja & Chapman, Colin A., 2016. "Spatial patterns of persistence for environmentally transmitted parasites: Effects of regional climate and local landscape," Ecological Modelling, Elsevier, vol. 338(C), pages 78-89.
    9. Norbert Brunner & Manfred Kühleitner & Werner Georg Nowak & Katharina Renner-Martin & Klaus Scheicher, 2019. "Comparing growth patterns of three species: Similarities and differences," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-9, October.

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