Presentation is loading. Please wait.

Presentation is loading. Please wait.

Logarithmic fitness trajectory emerges from hopping between MSs

Published byErkki Kahma Modified over 4 years ago

Similar presentations

Presentation on theme: "Logarithmic fitness trajectory emerges from hopping between MSs"— Presentation transcript:

1 Fig. 3 Logarithmic fitness trajectory emerges from hopping between MSs.
Logarithmic fitness trajectory emerges from hopping between MSs. (A) The average fitness trajectory from an initial MS (green circle) was found by constructing a Markov chain that includes all possible beneficial single mutants that a non-MS (blue circles) can go to and all possible beneficial double mutants that an MS (red circles) can go to. Here, we show a subset of the whole tree, with the thickness of the arrows proportional to −1/ log(λ), where λ is the transition rate. (B) The transition rates out of an MS (red histogram) are typically much slower than those out of a non-MS (blue histogram). (C) Average fitness trajectory increases logarithmically with time. The green points are data obtained from analyzing the Markov chain, while the black line is the fit to the logarithmic function F = 1 + a log(1 + bt). The red, blue, and purple dotted lines are examples of individual trajectories, with the crosses corresponding to fixation events. (D) Average trapping time 〈τ〉 scales approximately linearly with time, showing that as the population evolves, it enters MSs that are harder and harder to get out off. (E) The average τ (blue circles) of an MS seems to increase exponentially with its fitness. The black line is a fit to a straight line. (F) The average number of escape paths from an MS np seems to decrease exponentially with its fitness. The black line is a fit to the exponential function. In both (E) and (F), the averages are taken over states in a single Markov chain, with the weight of each state proportional to the probability of encountering that state, and the error bars represent 95% confidence intervals (parameters: L = 200, μ = 10−8, β = 0.9, Δ = 0.05, ρ = 0.05). Yipei Guo et al. Sci Adv 2019;5:eaav3842 Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Download ppt "Logarithmic fitness trajectory emerges from hopping between MSs"

Similar presentations

Fig. 4 Altitudinal distributions of ice thickness change (m year−1) for the 650 glaciers. Altitudinal distributions of ice thickness change (m year−1)

Fig. 4 Altitudinal distributions of ice thickness change (m year−1) for the 650 glaciers. Altitudinal distributions of ice thickness change (m year−1)
Fig. 6 Avoiding unphysical points in the LZ model.

Fig. 6 Avoiding unphysical points in the LZ model.
Fig. 5 Metafluorophores with different photostability.

Fig. 5 Metafluorophores with different photostability.
Fig. 4 Sequence divergence based on RAD sequences in five scaffolds containing genital QTL candidate genes between parapatric species with diverged genital.

Fig. 4 Sequence divergence based on RAD sequences in five scaffolds containing genital QTL candidate genes between parapatric species with diverged genital.
Fig. 2 Global production, use, and fate of polymer resins, synthetic fibers, and additives (1950 to 2015; in million metric tons). Global production, use,

Fig. 2 Global production, use, and fate of polymer resins, synthetic fibers, and additives (1950 to 2015; in million metric tons). Global production, use,
Fig. 3 Oil, gas, and FP water variations with time.

Fig. 3 Oil, gas, and FP water variations with time.
Fig. 5 Thermal conductivity of n-type ZrCoBi-based half-Heuslers.

Fig. 5 Thermal conductivity of n-type ZrCoBi-based half-Heuslers.
Fig. 1 Examples of experimental stimuli and behavioral performance.

Fig. 1 Examples of experimental stimuli and behavioral performance.
Fig. 1 NP-free Ch-CNC droplets.

Fig. 1 NP-free Ch-CNC droplets.
Fig. 4 Morphogenesis in the Ch-CNC host droplets and NP assemblies.

Fig. 4 Morphogenesis in the Ch-CNC host droplets and NP assemblies.
Fig. 2 Some examples of weekly forecasts (the number of the forecasts are reported on Table 1). Some examples of weekly forecasts (the number of the forecasts.

Fig. 2 Some examples of weekly forecasts (the number of the forecasts are reported on Table 1). Some examples of weekly forecasts (the number of the forecasts.
Fig. 4 Resynthesized complex boronic acid derivatives based on different scaffolds on a millimole scale and corresponding yields. Resynthesized complex.

Fig. 4 Resynthesized complex boronic acid derivatives based on different scaffolds on a millimole scale and corresponding yields. Resynthesized complex.
Fig. 6 Comparison of properties of water models.

Fig. 6 Comparison of properties of water models.
Fig. 2 Reference-fixing experiment, results.

Fig. 2 Reference-fixing experiment, results.
Fig. 3 TESC spectra with increasing coupling strength and plexciton energy diagram with QD detuning. TESC spectra with increasing coupling strength and.

Fig. 3 TESC spectra with increasing coupling strength and plexciton energy diagram with QD detuning. TESC spectra with increasing coupling strength and.
Fig. 4 Response of a swarm model.

Fig. 4 Response of a swarm model.
Fig. 3 Scan rate effects on the layer edge current.

Fig. 3 Scan rate effects on the layer edge current.
Fig. 3 Rotation experiment, setup.

Fig. 3 Rotation experiment, setup.
Fig. 1 Product lifetime distributions for the eight industrial use sectors plotted as log-normal probability distribution functions (PDF). Product lifetime.

Fig. 1 Product lifetime distributions for the eight industrial use sectors plotted as log-normal probability distribution functions (PDF). Product lifetime.
Fig. 1 Reference-fixing experiment, setup.

Fig. 1 Reference-fixing experiment, setup.

Similar presentations

Ads by Google