Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments - PubMed (original) (raw)

Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments

Ramon Xulvi-Brunet et al. Methods. 2016.

Abstract

In vitro selection experiments in biochemistry allow for the discovery of novel molecules capable of specific desired biochemical functions. However, this is not the only benefit we can obtain from such selection experiments. Since selection from a random library yields an unprecedented, and sometimes comprehensive, view of how a particular biochemical function is distributed across sequence space, selection experiments also provide data for creating and analyzing molecular fitness landscapes, which directly map function (phenotypes) to sequence information (genotypes). Given the importance of understanding the relationship between sequence and functional activity, reliable methods to build and analyze fitness landscapes are needed. Here, we present some statistical methods to extract this information from pools of RNA molecules. We also provide new computational tools to construct and study molecular fitness landscapes.

Keywords: Fitness landscape; In vitro evolution; RNA selection.

Copyright © 2016 Elsevier Inc. All rights reserved.

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Figures

Figure 1:

Figure 1:

Number of pairs of peaks joined by at least one pathway (pn) as a function of the step size (ss). Note that, since these data represent 11 peaks, the maximum number of pathways among the peaks is 11(111)/2 = 55. These data are taken from Replicate 1 of the experiment described previously. Note that peaks and pathways in the analysis here were defined according to a Hamming distance metric, for computational expediency while performing multiple analyses. Analysis based on Hamming distance and edit distance both identify major peaks, but analysis based on Hamming distance is less sensitive to minor peaks.

Figure 2:

Figure 2:

Extractor screenshot.

Figure 3:

Figure 3:

Counter screenshot.

Figure 4:

Figure 4:

Corrector screenshot.

Figure 5:

Figure 5:

Landscape Constructor screenshot.

Figure 6:

Figure 6:

Number of peaks (np) as a function of the cutoff distance (cd). The minimum number of sequences needed to define a peak was 5, and the distance definition was the Hamming distance. The data correspond to the data used in Figure 1. The behavior of the curve is robust to increases in the minimum number of sequences used to define a peak (for reasonable values) or the type of distance (Hamming vs. edit). Note the plateau between cd=3 and cd=8.

References

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