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• W4366605814 abstract "Measuring cell culture growth is a standard phenotyping technique in microbial research. It allows the determination of key parameters, such as exponential growth rate, acclimation time, and yield, which have been used extensively to study microorganisms’ gene function, physiology, and evolution. Diatoms, being unicellular eukaryotes, are commonly phenotyped in the same fashion as other microorganisms. However, the resulting growth data is distinct in many ways, making it challenging to apply standard parameter estimation methods used, for example, for heterotrophic bacteria. Diatom growth data analysis often translates to the manual, ad hoc, data fitting, typically involving linear regression for exponential growth rate. The rate value is subsequently regressed against the control factors in an experiment. The quality of rate estimate thus determines the meaningfulness of the following regression analysis, making the selection of the appropriate (most robust, accurate, and objective) method for rate calculation of utmost importance. We explored a suite of mathematical methods for specific growth rate estimation using sim-ulation-based trials. The experimental data set for the trials was generated in silico and included thousands of growth curves, featuring unique diatom-like characteristics combined with various challenges (e.g., culture crashes). The specific growth rate value was estimated for each of the simulated curves by parametric, nonparametric, and heuristic methods, and the performance of each of these methods was evaluated and ranked. We followed up with an empirical example of thermal responses in the chain-forming diatom Skeletonema subsalsum , demonstrating the possible consequences of choosing an inappropriate growth rate estimation method. We argue that the proposed approach is on the one hand scalable to high throughput studies, and on the other hand reduces bias and leads to higher reproducibility in smaller-scale studies. Finally, we provide a guide for selecting the most objective method for diatom growth data analysis on a case-by-case basis." @default.
• W4366605814 created "2023-04-23" @default.
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• W4366605814 date "2023-04-21" @default.
• W4366605814 modified "2023-09-26" @default.
• W4366605814 title "Diatom Growth" @default.
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• W4366605814 doi "https://doi.org/10.1002/9781119751939.ch6" @default.
• W4366605814 hasPublicationYear "2023" @default.
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