Biochemical systems theory was applied to mathematically model the inferred gene regulatory networks.
In the model, the inferred AC scores of these PWMs are used as predictors.
As each of the time series generated for each condition is an independent stochastic realisation of the model, the inferred parameter distributions can be expected to include the generating parameters, but this cannot be guaranteed.
2) In the original model the inferred parameters are scaled by current population size (N0) but in the second model the parameters are inferred separately using priors, following the approach of Tavaré et al [ 48] and Wilson and Balding [ 49]. 3) The original model is based on the assumption that all parameters other than mutation rate were identical across loci.
By applying a generalized growth epoch to the Gravel et al. (2011) model, the inferred parameters are generally in line with those from the generalized model based on Gazave et al. (2014), although some differences exist (Table 1), indicating that the assumption of ancient history can affect the inference of recent growth to some extent.
As with the generalized Gazave model, the inferred growth speed parameter from the generalized Gravel model, b = 1.22 (1.18–1.26), is also significantly larger than the exponential speed b = 1 (P-value ≪ 10 − 12, using a one-tailed z-test; Figure 4, C and D).
When the data of C. albicans and C. dubliniensis are analyzed in the frame of this model, the inferred values of show large variations during the S phase and appear to have globally increased before the divergence of C. albicans and C. dubliniensis lineages.
To verify the validity of the brain's inference and to decide which of the concurrently active models applies, the inferred causes are used to formulate predictions about the sensory input.
Therefore, despite statistically significant differences in model fit, the inferred genotypes under models M1 and M2 were very similar, M2 differing slightly from M1 by a higher level of uncertainty and a greater number of predicted heterozygotes.
Clearly, the Stress Model fits the inferred rates nicely over almost the whole range, in evident contrast with the Flexibility Model, for both pfANM (top panels) and ANM (bottom panels).
