In this paper, we show how system parameters can be derived by suitably matching the results of in vitro experiments with those obtained via simulations by using the BiNS2 simulator.
For each test performed, to exclude the presence of any false positive in our results, we calculated how many times similar P-values were expected by chance in 1,000 replicates of simulations by using the gene dropping method: this analysis replaces real data with simulated data, while maintaining the pedigree structure, allele frequencies and recombination fraction.
For each test performed for each specific phenotype, to exclude false positive results, we calculated the empirical P-value in 1,000 replicates of simulations by using the gene dropping method: this analysis replaces real data with simulated data, while maintaining the pedigree structure, allele frequencies and recombination fraction.
For each test performed, to exclude the presence of any results due to random chance, we calculated how many times similar P-values were expected by chance in 1,000 replicates of simulations by using the gene dropping method: this analysis replaces real data with simulated data, while maintaining the pedigree structure, allele frequencies and recombination fraction.
To rule out the presence of a false positive in our result, we calculated how many times P-values equal or inferior to the real one were expected by chance in 1,000 replicates of simulations by using the gene dropping method: this analysis replaces real data with simulated data, while maintaining the pedigree structure, allele frequencies and recombination fraction.
Its meteorological core, the Non-hydrostatic Multiscale Model on a B grid (NMMB33,35) allows for nested global-regional atmospheric simulations by using consistent physics and dynamics formulations.
The performance of the resulting controllers was verified in simulations by using the original nonlinear model.
The simulations by using a finite element method demonstrate the one-way transmission results.
