16 research outputs found
Effects of Epistasis and Pleiotropy on Fitness Landscapes
The factors that influence genetic architecture shape the structure of the
fitness landscape, and therefore play a large role in the evolutionary
dynamics. Here the NK model is used to investigate how epistasis and pleiotropy
-- key components of genetic architecture -- affect the structure of the
fitness landscape, and how they affect the ability of evolving populations to
adapt despite the difficulty of crossing valleys present in rugged landscapes.
Populations are seen to make use of epistatic interactions and pleiotropy to
attain higher fitness, and are not inhibited by the fact that valleys have to
be crossed to reach peaks of higher fitness.Comment: 10 pages, 6 figures. To appear in "Origin of Life and Evolutionary
Mechanisms" (P. Pontarotti, ed.). Evolutionary Biology: 16th Meeting 2012,
Springer-Verla
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
A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems
On the networked architecture of genotype spaces and its critical effects on molecular evolution
Gene expression analysis of E. coli strains provides insights into the role of gene regulation in diversification
Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing
Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any 'prior knowledge' of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information)