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The data from this portion was then used to generate models predicting growth and development under fluctuating temperatures as described in the modelling methods section.
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Spatial individual-tree models predict growth and mortality rates as functions of the target tree size, and of a competition or resource capture index that encapsulates neighbourhood effects.
The permutation test is performed by permuting the models' predicted growth rates (after reaction knock down) 1000 times.
These comparisons results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment or the model predicts no growth when the experiment reveals growth.
Sharp-interface modeling predicts growth rates over the whole range of undercooling.
If a flux distribution could be found, the model predicted growth, otherwise it predicted no growth.
In some cases, the model predicted growth when pathways with poorly characterized genes were required.
Model predicted growth rates are highly sensitive to nutrient uptake rates which are unknown in the host celll.
The individuals exhibit observed growth rates of 5 and −2 mm/year with model predicted growth rates of 2.3 and 1.3 mm/year, respectively.
For a particular pair of carbon source and electron acceptor, growth phenotypes were qualitatively classified as growth if the model predicted growth rate is positive and no growth if the predicted growth rate is zero.
The 16 and 19 carbon sources with discrepancies between in silico and experimental results in at least one model (shown in Figure 5) fall into two categories i) when strains did not grow and the model predicted growth (false positive), and ii) instances where strains grew and the model predicted no growth (false negative).
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