A related and novel approach that we have developed (Wilkinson et al. 2008) (and see Brown et al. 2010) also evolves estimates of parameters, but in a somewhat different way.
Our model could be used to carry out such a test as part of future research, by comparing estimates of effective population size when the modelled population is evolving to estimates when evolution is artificially precluded.
We show the influence of these variables on the motor system during preparation for action and the advantage of our computationally informed model over categorical predictors that are not informed about the time-evolving estimates of probabilistic context.
Finally, we note that other assumptions about the evolutionary model might also influence time-to-evolve estimates.
There is a clear trend of anterior patterns to have lower time-to-evolve estimates, while central and posterior patterns have larger estimates (fig. 1 C, bottom).
As noted above, time-to-evolve estimates for CRMs of different expression patterns vary greatly, by at least one order of magnitude.
A visual inspection (fig. 1 C) suggested that expression in the anterior domain of the embryo marks smaller time-to-evolve estimates.
For instance, reducing the selection scale by a factor of 10 (K = 5) results in time-to-evolve estimates increasing by less than a factor of 10.
We interpret these observations to suggest that the number of HB sites in the initial random sequences influences the fitness of those sequences for certain target patterns, and therefore their time-to-evolve estimates.
We found that binding site content of the TF HB has a strong positive correlation with time-to-evolve estimates (Pearson CC = 0.70, P = 1.5 × 10 − 5 ; fig. 3 A).
