Abstract
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).
Highlights
A common circumstance in biotechnology [1] is that we have an entity that we wish to improve for some specific purposes, and this is largely done by genetic means, involving either the mutation of a single parent entity or by means that involve genetic recombination
We note too that we are usually interested in optimising for multiple traits, which may be largely independent of or at best only partially linked to each other
2, 3, 4 and 5, illustrating fitness and entropy, it can be seen the breeder algorithm displays inferior performance relative to the other evaluated algorithms, indicating that strong selection pressure without recombination is not conducive to optimisation on the modified NK-landscapes; whilst the fitness rises rapidly, the entropy drop almost immediately to near zero, indicating that population diversity is low, conditions typically seen with premature convergence to a local maximum
Summary
A common circumstance in biotechnology [1] is that we have an entity (be it a protein, nucleic acid or organism) that we wish to improve for some specific purposes, and this is largely done by genetic means, involving either the mutation of a single parent entity or (if mating is possible) by means that involve genetic recombination. We note too that we are usually interested in optimising for multiple traits, which may be largely independent of or at best only partially linked to each other (e.g. disease resistance in a crop plant is essentially independent of the metabolic processes governing primary yield, but would potentially contribute to overall yield, and we might select for larger roots [7,8] separately from the agronomic benefits such as drought tolerance that they might bring) This makes our searches for improved strains a multiobjective optimisation problem [9]. Plant and animal breeding holds considerable potential for improving food security, a topic of substantial current interest [10,11,12,13,14,15,16,17]
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