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

Matrix-Variate Discriminative Analysis, Integrative Hypothesis Testing, and Geno-Pheno A5 Analyzer

Abstract. A general perspective is provided on both on hypothesis testing and discriminative analyses, by which matrix-variate discriminative analyses are pro-posed based on the matrix normal distribution, featured by a bi-linear extension of Fisher linear discriminant analysis and a further extension to binary variables. Moreover, a general formulation is proposed for integrative hypothesis testing and five typical categories are summarized. Furthermore, major techniques for varia-ble selection are briefly elaborated. Finally, taking analyses of gene expression and exome sequencing as examples, we further propose a general procedure called Geno-Pheno A5 Analyzer for integrative discriminant analysis

Fixation of new mutations in small populations

Evolution proceeds as the result of a balance between a few basic processes: mutation, selection, migration, genetic drift, and recombination. Mutation is the ultimate source of all the genetic variation on which selection may act; it is therefore essential to evolution. Mutations carry a large cost, though; almost all are deleterious, reducing the fitness of the organisms in which they occur (see Chapter 7). Mutation is therefore both a source of good and ill for a population (Lande 1995). The overall effect of mutation on a population is strongly dependent on the population size. A large population has many new mutations in each generation, and therefore the probability is high that it will obtain new favorable mutations. This large population also has effective selection against the bad mutations that occur; deleterious mutations in a large population are kept at a low frequency within a balance between the forces of selection and those of mutation. A population with relatively fewer individuals, however, will have lower fitness on average, not only because fewer beneficial mutations arise, but also because deleterious mutations are more likely to reach high frequencies through random genetic drift. This shift in the balance between fixation of beneficial and deleterious mutations can result in a decline in the fitness of individuals in a small population and, ultimately, may lead to the extinction of that population. As such, a change in population size may determine the ultimate fate of a species affected by anthropogenic change

Experimental evolution.

Experimental evolution is the study of evolutionary processes occurring in experimental populations in response to conditions imposed by the experimenter. This research approach is increasingly used to study adaptation, estimate evolutionary parameters, and test diverse evolutionary hypotheses. Long applied in vaccine development, experimental evolution also finds new applications in biotechnology. Recent technological developments provide a path towards detailed understanding of the genomic and molecular basis of experimental evolutionary change, while new findings raise new questions that can be addressed with this approach. However, experimental evolution has important limitations, and the interpretation of results is subject to caveats resulting from small population sizes, limited timescales, the simplified nature of laboratory environments, and, in some cases, the potential to misinterpret the selective forces and other processes at work