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At the core is a Monte Carlo (MC) simulation that explores certain ranges of the parametric space around a given initial parameter value and generates samples of numerous parameter vectors.
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Set initial values for parameter vectors.
This enabled different combinations of parameter vectors to be explored.
B, Γ and D denote the accompanying parameter vectors.
This produces a parameter grid that contains a total of 625 different parameter vectors.
The parameter "pool" φ is therefore simply a concatenation of all model parameter vectors.
Starting from each of the 15 successful parameter vectors chosen from the DE run, we introduced one of the parameter perturbations to create a "new" parameter vector.
In total, from the 15 initial parameter vectors we created 20115 new parameter vectors (15 sets × 9 perturbation levels × 149 varying model parameters) and ran 119 simulations per vector.
In addition to producing highly successful combinations of parameter values, we analyze the results to determine the parameters that are most critical for matching experimental outcomes and the most competitive strains whose correct outcome with a given parameter vector forces numerous other strains to have incorrect outcomes.
the parameter vector.
Numerous parameters can make the optimization problems more complicated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com