Skip to main content
. 2024 Dec 27;13:RP97350. doi: 10.7554/eLife.97350

Figure 4. Simulations under the Wright-Fisher model with expiring fitness.

(A) Average frequency dynamics of immune escape mutations that are found to cross the frequency threshold x=0.5, for four different rates of fitness decay. If the growth advantage is lost rapidly (high ν/s0), the trajectories crossing x have little inertia, while stable growth advantage (small ν/s0) leads to steadily increasing frequencies. (B, C, D) Ultimate probability of pfix(x) of trajectories found crossing frequency threshold x. Each panel corresponds to a different rate of emergence of immune escape variants, with four rates of fitness decay per panel. Increased clonal interference ρ/s0 and fitness decay ν/s0 both result in a gradual loss of predictability. We use s0=0.03. (E) Time to most recent common ancestor TMRCA for the simulated population, as a function of the prediction obtained using the random walk Ne=1/ρβ2. Points correspond to different choices of parameters ρ and Pβ, and a darker color indicates a higher probability of overlap as computed in Appendix 2.2.

Figure 4.

Figure 4—figure supplement 1. Probability of fixation of mutation Probability of fixation of mutations pfix(x) of mutation frequency trajectories found crossing the frequency threshold x.

Figure 4—figure supplement 1.

Fitness effects are exponentially distributed with fixed scale s0=0.03. The blue to red gradient in colors corresponds to the increasing rate ρ at which mutations are introduced. Strong clonal interference regime is obtained when ρ/s0>1, in which case good mutations are introduced in close succession and compete for fixation. At low ρ/s0, trajectories are very predictable and an increasing trajectory almost certainly fixes. Even for the highest ρ/s0, pfix(x) remains significantly larger than x and dynamics are visibly not neutral.