6,263 research outputs found
Fluctuation Domains in Adaptive Evolution
We derive an expression for the variation between parallel trajectories in
phenotypic evolution, extending the well known result that predicts the mean
evolutionary path in adaptive dynamics or quantitative genetics. We show how
this expression gives rise to the notion of fluctuation domains - parts of the
fitness landscape where the rate of evolution is very predictable (due to
fluctuation dissipation) and parts where it is highly variable (due to
fluctuation enhancement). These fluctuation domains are determined by the
curvature of the fitness landscape. Regions of the fitness landscape with
positive curvature, such as adaptive valleys or branching points, experience
enhancement. Regions with negative curvature, such as adaptive peaks,
experience dissipation. We explore these dynamics in the ecological scenarios
of implicit and explicit competition for a limiting resource
Genealogical inferences based on comparison of modern and ancient DNA
The study of genetic variation within and between populations can help us understand
aspects of human demographic history over the past thousands of years, i.e. well beyond the timescales
of historical evidence. Demographic and evolutionary dynamics influence the distribution of
the observed genetic diversity, and so one can retrospectively reconstruct episodes in population
history on the basis of genetic diversity data. One way to do this is to make extensive use of
simulations, considering evolution as a stochastic process in which the genetic data are modeled
as random variables. The simulation of genetic data under various scenarios allows one to explore
how demographic and evolutionary parameters can affect genetic variation, also making it
possible to approximately estimate the historical parameters that produced the observed data. To
this aim, many statistical approaches have been developed, but, when models are complex or
datasets are large, they often become computationally expensive, or analytically intractable.
Approximate Bayesian Computation (ABC) methods overcome these problems allowing, for the
first time, to analyze large datasets and to interpret them in the light of realistic (i.e. complex)
models, thus enabling the probabilistic comparison among different models of evolution, the
simultaneous estimation of demographic and evolutionary parameters, and the quantitative
evaluation of the results credibility. In this context, we analyzed datasets of modern and ancient
genetic variation in order to understand the demographic histories of these populations, to
highlight traces of past genetic variation in modern populations, and to evaluate whether, and to
what extent, ancient and modern populations that have lived in the same place in different period
of times can be considered genealogically related. We tried to address three anthropological
questions, namely the interaction of anatomically modern humans with archaic forms (i.e.
Neandertals in Europe), evidence for genealogical continuity in Sardinia since the Bronze-age, and
the origins and evolution of the Etruscan population. Within the ABC framework, in each of the
three studies, we explicitly compared several models, differing for the demographic processes and
the genealogical relationship among population, to identify the model best accounting for the
observed variation, and to estimate its demographic and evolutionary parameters. This way, it has
been possible to shed light on past population history and to address questions about the nature
and the extent of genealogical links between modern and ancient populations, clarifying aspects
of human history that have long been controversial in population genetics and evolutionary
biology
A framework for evolutionary systems biology
<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p
Multiscale modeling in biology
The 1966 science-fction film Fantastic Voyage captured the public imagination with a clever idea: what fantastic things might we see and do if we could minaturize ourselves and travel through the bloodstream as corpuscles do? (This being Hollywood, the answer was that we'd save a fellow scientist from evildoers.
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