Fig 1.
Genotype-phenotype landscapes of transcription factor binding affinities and their composition bias.
(a) Transitions are mutations from a purine to a purine, or from a pyrimidine to a pyrimidine. Transversions are mutations from a purine to a pyrimidine, or vice versa. (b) The dominant genotype network for the yeast transcription factor Sum1. Each node corresponds to a DNA sequence that binds Sum1 with an E-score >0.35. Node size is proportional to number of connections (bigger = more) and color to binding affinity (darker = higher). Two nodes are connected by an edge if their corresponding sequences differ by a single point mutation (e.g., see inset), either a transition (blue edges) or a transversion (green edges). (c) Schematic representation of a genotype-phenotype landscape, and an accessible mutational path to the global peak involving four transversions and one transition.
Fig 2.
Accessible mutational paths exhibit composition bias toward transversions.
(a) The black bars show the distribution of composition bias across entire landscapes. The white bars show the distribution of composition bias across accessible mutational paths to the global peak, starting from the 10% of binding sites with the lowest affinities in each landscape. Gray indicates the overlap in the distributions. Data pertain to all 746 landscapes. (b) Composition bias of accessible mutational paths, with landscapes grouped by DNA binding domain structural class. Numbers in parentheses indicate the number of transcription factors per class in our dataset.
Fig 3.
Mutation bias interacts with composition bias to influence landscape navigability.
The probability Ppeak of reaching the global peak is shown for 19 different values of the mutation bias parameter α. The solid vertical lines indicate no bias in mutation supply (α = 0.5) and the dashed vertical lines indicate the value of α that maximizes Ppeak. Landscapes are grouped based on their composition bias and the distribution of composition bias per panel is shown on top of each panel. The number of landscapes per panel is indicated is the bottom left corner.
Fig 4.
Mutation bias interacts with composition bias to impact the predictability of evolution.
(a) Distribution of the mutation bias parameter α that minimizes path entropy for each landscape. (b) Mutation bias parameter that minimizes path entropy, shown in relation to composition bias. (c) Relative entropy change (entropyno bias/entropymin), shown in relation to the mutation bias parameter α that minimizes path entropy. (d) Distribution of the mutation bias parameter α that maximizes path entropy for each landscape. (e) Mutation bias parameter that maximizes path entropy, shown in relation to composition bias. (f) Relative entropy change (entropymax/entropyno bias), shown in relation to the mutation bias parameter α that maximizes path entropy. Data in all panels pertain to all 746 landscapes.
Fig 5.
Evolving polymorphic populations are more sensitive to changes in mutation bias than to the stochastic nature of the evolutionary simulations.
(a) Schematic figure of our experimental design. For each landscape and combination of population size and mutation rate (Nμ), we considered 10 replicates for each of 10 different initial conditions and 19 values of the mutation bias parameter α. Importantly, we used the replicate number to seed the random number generator of each evolutionary simulation, facilitating the comparison of variation across replicates versus across the mutation bias parameter α. For example, the matrix elements indicated in gray contain the information necessary to compare the effects of the mutation bias parameter with the stochasticity of the evolutionary simulations, for one initial condition. (b) Overlap coefficient for pairs of evolved populations that differ in random number generator seed (“Replicates”) or in mutation bias parameter (“Mutation bias”). Notches indicate medians, whiskers indicate the 25th and 75th percentiles, and cross symbols indicate outliers. (c) Overlap coefficient for pairs of evolved populations, shown in relation to the difference in their mutation bias parameters.
Fig 6.
Mutation bias interacts with composition bias to influence the evolution of genetic diversity and mutational robustness.
(a-e) The average genetic diversity and (f-j) mutational robustness of evolved populations at steady state is shown for 19 different values of the mutation bias parameter α, and for each of three different values of mutation supply Nμ (see legend). The solid vertical lines indicate no bias in mutation supply (α = 0.5). Landscapes are grouped based on their composition bias and the distribution of composition bias per panel is shown on top of each panel as in Fig 3.