1,996 research outputs found
Predictability of evolutionary trajectories in fitness landscapes
Experimental studies on enzyme evolution show that only a small fraction of
all possible mutation trajectories are accessible to evolution. However, these
experiments deal with individual enzymes and explore a tiny part of the fitness
landscape. We report an exhaustive analysis of fitness landscapes constructed
with an off-lattice model of protein folding where fitness is equated with
robustness to misfolding. This model mimics the essential features of the
interactions between amino acids, is consistent with the key paradigms of
protein folding and reproduces the universal distribution of evolutionary rates
among orthologous proteins. We introduce mean path divergence as a quantitative
measure of the degree to which the starting and ending points determine the
path of evolution in fitness landscapes. Global measures of landscape roughness
are good predictors of path divergence in all studied landscapes: the mean path
divergence is greater in smooth landscapes than in rough ones. The
model-derived and experimental landscapes are significantly smoother than
random landscapes and resemble additive landscapes perturbed with moderate
amounts of noise; thus, these landscapes are substantially robust to mutation.
The model landscapes show a deficit of suboptimal peaks even compared with
noisy additive landscapes with similar overall roughness. We suggest that
smoothness and the substantial deficit of peaks in the fitness landscapes of
protein evolution are fundamental consequences of the physics of protein
folding.Comment: 14 pages, 7 figure
Predicting evolution and visualizing high-dimensional fitness landscapes
The tempo and mode of an adaptive process is strongly determined by the
structure of the fitness landscape that underlies it. In order to be able to
predict evolutionary outcomes (even on the short term), we must know more about
the nature of realistic fitness landscapes than we do today. For example, in
order to know whether evolution is predominantly taking paths that move upwards
in fitness and along neutral ridges, or else entails a significant number of
valley crossings, we need to be able to visualize these landscapes: we must
determine whether there are peaks in the landscape, where these peaks are
located with respect to one another, and whether evolutionary paths can connect
them. This is a difficult task because genetic fitness landscapes (as opposed
to those based on traits) are high-dimensional, and tools for visualizing such
landscapes are lacking. In this contribution, we focus on the predictability of
evolution on rugged genetic fitness landscapes, and determine that peaks in
such landscapes are highly clustered: high peaks are predominantly close to
other high peaks. As a consequence, the valleys separating such peaks are
shallow and narrow, such that evolutionary trajectories towards the highest
peak in the landscape can be achieved via a series of valley crossingsComment: 12 pages, 7 figures. To appear in "Recent Advances in the Theory and
Application of Fitness Landscapes" (A. Engelbrecht and H. Richter, eds.).
Springer Series in Emergence, Complexity, and Computation, 201
Every which way? On predicting tumor evolution using cancer progression models
Successful prediction of the likely paths of tumor progression is valuable for diagnostic,
prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and
thus CPMs encode the paths of tumor progression. Here we analyze the performance of
four CPMs to examine whether they can be used to predict the true distribution of paths of
tumor progression and to estimate evolutionary unpredictability. Employing simulations we
show that if fitness landscapes are single peaked (have a single fitness maximum) there is
good agreement between true and predicted distributions of paths of tumor progression
when sample sizes are large, but performance is poor with the currently common much
smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all
cases, detection regime (when tumors are sampled) is a key determinant of performance.
Estimates of evolutionary unpredictability from the best performing CPM, among the four
examined, tend to overestimate the true unpredictability and the bias is affected by detection
regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability
for several of the data sets. But most of the predictions of paths of tumor progression are
very unreliable, and unreliability increases with the number of features analyzed. Our results
indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and
emphasize the need for methodological work that can account for the probably multi-peaked
fitness landscapes in cancerWork partially supported by BFU2015-
67302-R (MINECO/FEDER, EU) to RDU. CV
supported by PEJD-2016-BMD-2116 from
Comunidad de Madrid to RD
Statistical Physics of Evolutionary Trajectories on Fitness Landscapes
Random walks on multidimensional nonlinear landscapes are of interest in many
areas of science and engineering. In particular, properties of adaptive
trajectories on fitness landscapes determine population fates and thus play a
central role in evolutionary theory. The topography of fitness landscapes and
its effect on evolutionary dynamics have been extensively studied in the
literature. We will survey the current research knowledge in this field,
focusing on a recently developed systematic approach to characterizing path
lengths, mean first-passage times, and other statistics of the path ensemble.
This approach, based on general techniques from statistical physics, is
applicable to landscapes of arbitrary complexity and structure. It is
especially well-suited to quantifying the diversity of stochastic trajectories
and repeatability of evolutionary events. We demonstrate this methodology using
a biophysical model of protein evolution that describes how proteins maintain
stability while evolving new functions
Universality and predictability in molecular quantitative genetics
Molecular traits, such as gene expression levels or protein binding
affinities, are increasingly accessible to quantitative measurement by modern
high-throughput techniques. Such traits measure molecular functions and, from
an evolutionary point of view, are important as targets of natural selection.
We review recent developments in evolutionary theory and experiments that are
expected to become building blocks of a quantitative genetics of molecular
traits. We focus on universal evolutionary characteristics: these are largely
independent of a trait's genetic basis, which is often at least partially
unknown. We show that universal measurements can be used to infer selection on
a quantitative trait, which determines its evolutionary mode of conservation or
adaptation. Furthermore, universality is closely linked to predictability of
trait evolution across lineages. We argue that universal trait statistics
extends over a range of cellular scales and opens new avenues of quantitative
evolutionary systems biology
Mutation supply and the repeatability of selection for antibiotic resistance
Whether evolution can be predicted is a key question in evolutionary biology.
Here we set out to better understand the repeatability of evolution. We
explored experimentally the effect of mutation supply and the strength of
selective pressure on the repeatability of selection from standing genetic
variation. Different sizes of mutant libraries of an antibiotic resistance
gene, TEM-1 -lactamase in Escherichia coli, were subjected to different
antibiotic concentrations. We determined whether populations went extinct or
survived, and sequenced the TEM gene of the surviving populations. The
distribution of mutations per allele in our mutant libraries- generated by
error-prone PCR- followed a Poisson distribution. Extinction patterns could be
explained by a simple stochastic model that assumed the sampling of beneficial
mutations was key for survival. In most surviving populations, alleles
containing at least one known large-effect beneficial mutation were present.
These genotype data also support a model which only invokes sampling effects to
describe the occurrence of alleles containing large-effect driver mutations.
Hence, evolution is largely predictable given cursory knowledge of mutational
fitness effects, the mutation rate and population size. There were no clear
trends in the repeatability of selected mutants when we considered all
mutations present. However, when only known large-effect mutations were
considered, the outcome of selection is less repeatable for large libraries, in
contrast to expectations. Furthermore, we show experimentally that alleles
carrying multiple mutations selected from large libraries confer higher
resistance levels relative to alleles with only a known large-effect mutation,
suggesting that the scarcity of high-resistance alleles carrying multiple
mutations may contribute to the decrease in repeatability at large library
sizes.Comment: 31pages, 9 figure
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