High-Dimensional Adaptive Landscapes Facilitate Evolutionary Innovation

2013 ◽  
pp. 271-282 ◽  
Author(s):  
Andreas Wagner
Keyword(s):  
High Dimensional ◽  
Adaptive Landscapes ◽  
Evolutionary Innovation

scholarly journals Relative importance of molecular, morphological and paleontological data in understanding evolutionary innovations

1992 ◽  
Vol 6 ◽  
pp. 199-199
Author(s):  
Charles R. Marshall
Keyword(s):  
18S Rrna ◽  
A Priori ◽  
Parallel Evolution ◽  
Evolutionary Process ◽  
Data Sets ◽  
Paleontological Data ◽  
Adaptive Landscapes ◽  
Evolutionary Innovation ◽  
The One ◽  
Quality Character

Recently there have been increasing efforts to integrate morphological, molecular and paleontological data in assessing evolutionary relationships. In some instances molecules appear more reliable than morphology. For example, a DNA-DNA hybridization phylogeny of clypeasteroid echinoids appears superior to conflicting morphological phylogenies, because: 1) Of all the nodes on the morphological tree, the one drawn into question by the DNA data is the least supported (the characters are few and of relatively low quality; character quality was judged using Remane's a priori homology recognition criteria). 2) A case can be made for the conflicting characters being functional correlates. 3) The morphological tree only finds support in 1% of 500 bootstrap analyses of the DNA data. 4) On the DNA tree sister groups occupy the same paleobiogeographic realm while on the morphological tree sister groups occupy completely disparate paleobiogeographic realms.However, in other instances morphology appears more reliable than molecules. For example, a recent 18S rRNA phylogeny of amniotes unites birds and mammals to the exclusion of crocodiles. Paleontologically this result seemed highly unlikely and it was suspected that some sort of substitution bias was responsible for the 18S rRNA result. Not only were pronounced substitution biases found, but when the 18S rRNA sequences were analyzed with an algorithm designed to deal with substitution biases a paleontologically more ‘reasonable’ tree resulted (birds were closer to crocodiles, not mammals).By comparing independent data sets, it appears that both morphological features and molecules may evolve less than parsimoniously. More significantly, in these cases the unanticipated parallel evolution has an identifiable biological 'signature’. It is perhaps disappointing that the evolutionary process may produce more parallel evolution than is implied by most-parsimonious trees. But when analyzed carefully there appears to be a coherency to the unanticipated parallel evolution, a coherency that may help us understand the structure of the adaptive landscapes within which evolutionary innovation arises.

Homology, Genes, and Evolutionary Innovation

2014 ◽  
Author(s):  
Günter P. Wagner
Keyword(s):  
Evolutionary Biology ◽  
Regulatory Networks ◽  
Biological Diversity ◽  
Cell Types ◽  
Origin And Evolution ◽  
Major Transitions ◽  
Form And Function ◽  
Historical Continuity ◽  
Evolutionary Innovation ◽  
Character Identity

Homology—a similar trait shared by different species and derived from common ancestry, such as a seal's fin and a bird's wing—is one of the most fundamental yet challenging concepts in evolutionary biology. This book provides the first mechanistically based theory of what homology is and how it arises in evolution. The book argues that homology, or character identity, can be explained through the historical continuity of character identity networks—that is, the gene regulatory networks that enable differential gene expression. It shows how character identity is independent of the form and function of the character itself because the same network can activate different effector genes and thus control the development of different shapes, sizes, and qualities of the character. Demonstrating how this theoretical model can provide a foundation for understanding the evolutionary origin of novel characters, the book applies it to the origin and evolution of specific systems, such as cell types; skin, hair, and feathers; limbs and digits; and flowers. The first major synthesis of homology to be published in decades, this book reveals how a mechanistically based theory can serve as a unifying concept for any branch of science concerned with the structure and development of organisms, and how it can help explain major transitions in evolution and broad patterns of biological diversity.

Preface to the special issue, CMAM 2011, no. 3.

2011 ◽  
Vol 11 (3) ◽  
pp. 272
Author(s):  
Ivan Gavrilyuk ◽  
Boris Khoromskij ◽  
Eugene Tyrtyshnikov
Keyword(s):  
Separation Of Variables ◽  
Numerical Algorithms ◽  
Multilevel Methods ◽  
High Dimensional ◽  
Special Issue ◽  
High Dimensions ◽  
Guiding Principle ◽  
Tensor Methods ◽  
Closed World ◽  
The One

Abstract In the recent years, multidimensional numerical simulations with tensor-structured data formats have been recognized as the basic concept for breaking the "curse of dimensionality". Modern applications of tensor methods include the challenging high-dimensional problems of material sciences, bio-science, stochastic modeling, signal processing, machine learning, and data mining, financial mathematics, etc. The guiding principle of the tensor methods is an approximation of multivariate functions and operators with some separation of variables to keep the computational process in a low parametric tensor-structured manifold. Tensors structures had been wildly used as models of data and discussed in the contexts of differential geometry, mechanics, algebraic geometry, data analysis etc. before tensor methods recently have penetrated into numerical computations. On the one hand, the existing tensor representation formats remained to be of a limited use in many high-dimensional problems because of lack of sufficiently reliable and fast software. On the other hand, for moderate dimensional problems (e.g. in "ab-initio" quantum chemistry) as well as for selected model problems of very high dimensions, the application of traditional canonical and Tucker formats in combination with the ideas of multilevel methods has led to the new efficient algorithms. The recent progress in tensor numerical methods is achieved with new representation formats now known as "tensor-train representations" and "hierarchical Tucker representations". Note that the formats themselves could have been picked up earlier in the literature on the modeling of quantum systems. Until 2009 they lived in a closed world of those quantum theory publications and never trespassed the territory of numerical analysis. The tremendous progress during the very recent years shows the new tensor tools in various applications and in the development of these tools and study of their approximation and algebraic properties. This special issue treats tensors as a base for efficient numerical algorithms in various modern applications and with special emphases on the new representation formats.

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