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A given starting point, that is a given initial diagenesis model, is randomly generated (Fig. 4b).
For each step in the search, part of the model is randomly perturbed, the effect on the fits to the observations is assessed, and if the fits improve, we keep the new model, otherwise we try another perturbation.
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Specifically, the coordinates of the refined model were randomly shifted by 0.2 Å using Phenix PDB tool.
This model was then reconstructed again by tenfold cross-validation: all images used in the reference model were randomly partitioned into 10 sub-samples of equal size.
The parameters that define the initial abundance of biological species in the WT model were randomly varied between 0.1 and 10 fold in order to reproduce the temporal activation data on Y992F cell signaling.
Next, residuals under the reduced model were randomly permuted 10,000 times.
Residual plots of the model were randomly distributed without any trends (not shown), validating the quadratic models.
Where a gene was annotated as having more than one transcript or gene model, one transcript and model was randomly selected.
Residuals resulting from the differences between the observed and predicted risks were also calculated and mapped to establish whether errors in the model were randomly distributed, thereby supporting the validity of the model throughout the continent.
Since short-term costs in our model were randomly distributed throughout the 10-year time horizon, to simplify the model, we only discounted the long-term costs, such as long-term disability costs and costs due to death.
In our simulation study, the cohort comprised of a population of 100 000 individuals (of age 20) who, in the baseline model, were randomly placed in at least one and up to three groups.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com