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In parallel, daily above-ground biomass production (NPPshoot) was estimated using a logistic growth equation, fitted on periodic biomass samples.
We developed a regional-scale succession model based on empirical information on fire return intervals, fire sizes (using a reverse Weibull function) and post-fire successional trends (using a logistic growth function), based on an extensive dataset collected in a 65,000 km2 section of the boreal forest located in eastern Quebec.
Homeostatic proliferation is modeled using a logistic growth model limited by viral load.
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FA assembly rate was modeled using a logistic function (exponential growth at early time points which plateaus to a maximum) and the final continuous phase of FA disassembly was modeled as simple exponential decay.
In batch culture, the changes over time of the cell number are classically analysed using a logistic model (exponential growth followed by a stationary phase) in order to estimate population dynamics parameters.
In their study Wittgens et al. (2011) described biomass growth using a logistic equation and glucose metabolization as well as rhamnolipid formation using biomass dependent ordinary differential equations (ODEs).
This was simulated using a logistic regression, where the probability of progressing due to non-growth reasons at each timepoint, is specified.
In particular, the growth term of the proliferative cell compartment is modeled using a logistic term.
The bacterial loads from fly lines with bacterial growth plateau greater than or equal to 10 CFU were fit well using a logistic curve (R > 0.7), as anticipated.
Those variables were analyzed using a logistic regression.
These tendencies were statistically analyzed using a logistic regression model.
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