Fig 3. Exploring the state space of expression models for allopolyploids.

In A&B, we propose two meta-models of gene expression, a simple one (A) involving only the polymerase binding a target gene (similar to Fig 1A but without DNA breathing), and a more complex one that follows Fig 2 with slightly different parameter values (Methods). In each case, we generate two random models A₁ and A₂ by selecting uniform random values for the model parameters across the ranges listed (Methods). After computing the steady state mRNA concentration for A₁, we use an approximate gradient method to bring A₂ to equal steady state mRNA concentration with A₁ (Methods). The resulting parameter values are normalized and the Euclidian distance between A₁ and A₂ is computed (Methods). We then construct an allopolyploid hybrid of A₁ and A₂ and compute resulting mRNA bias B C. Distribution of model-pair distances for 1000 simulations of the simple and complex models from A&B. On x is the distance between the model pairs (y-axis in D), on y are the frequencies of those distances. D. One thousand random pairs of genome models were created from each meta-model and their hybrids simulated. The plot shows the relationship between the distance between the model pairs (y-axis) and expression bias B (x-axis: note the log scale). E. Histograms of B, plotted on a log-scale (c.f., C).