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As evolutionary biologists have always been concerned with the genetic basis for the emergence of complex phenotypes, advances in genomics and systems biology are facilitating a paradigm shift of molecular evolutionary biology toward a better understanding of the relationship of genotypes and phenotypes. From an evolutionary perspective, the central question is whether natural selection is a necessary and/or sufficient force to explain the emergence of genomic and cellular features that underlie the building of complex organisms. Lynch has criticized the adaptive hypothesis for the origins of organismal complexity, claiming that nothing in evolution makes sense in light of population genetics that takes the effects of mutation, genetic drift, and natural selection into account. The importance of mutation types and genetic drifts on the phenotype evolution has also been emphasized by Nei and his associates. One plausible approach to resolving these fundamental issues is to model the features of biological complexity as parameters instead of emerged properties, under the principle of population genetics and molecular evolution. This chapter discusses some recent results in this trend.

Molecular evolution is the study of the process of evolution at the level of DNA, RNA, and proteins, in which the neutral or nearly neutral evolution model has provided the theoretical basis. Yet, the role of positive selection at the molecular level remains a controversial issue. Recent advances in genomics, including whole-genome sequencing, high-throughput protein characterization, and bioinformatics have led to a dramatic increase in studies in comparative and evolutionary genomics. This chapter introduces some widely-used methods in genomic analysis. These include distance method, parsimony methods, maximum-likelihood methods, Bayesian methods, and ancestral sequence inference.

The available web-based genome resources provide great opportunities for biomedical scientists to identify functional elements in a particular genome region or explore the evolutionary pattern of genome dynamics. This chapter gives a brief review of the availability of web-based databases and software for selected important topics. Topics covered include bioinformatic resources for evolutionary genomics, basic statistics for homologous search, sequence alignment, microarrays and statistics, and Markov chain Monte Carlo algorithms.

This chapter characterizes the consequences of a shift of focus from individual genes to gene networks. It describes some of the opportunities and challenges that the genomic era brings to evolutionary biology, and some of the ways current research into genome evolution is extending the Modern Synthesis. The chapter also discusses three extensions to the Modern Synthesis that are emerging out of the tumult and excitement of evolutionary genomics. It suggests that applying the traditional approaches of population genetics, evolutionary genetics, and molecular evolution to genomic data sets presents nontrivial challenges.

This chapter focuses on studies of the evolutionary genomics of molluscans. It provides an overview of the state of molluscan mitochondrial genomics and outlines the promise of a broadening of this data set. It explains how comparisons of mitochondrial genomes have reconstructed the relationships among Annelida, Sipuncula, and Mollusca and describes upcoming projects involving the sequencing of whole mollusc nuclear genome.

Evolutionary rates not only reflect the tempo at which genotypes, phenotypes, and species lineages are transformed over time but also offer valuable insight into the evolutionary processes involved in divergence. For the last century and a half, biologists have been, and to a large degree still remain, steeped in the paradigm of slow and steady gradual evolutionary change. New findings and technologies are beginning to change this view as significant rate variations are seen in a large number of examples across and within all levels of biological organization. This introductory chapter provides a brief historical account and broad survey of recent findings on evolution rates and highlights examples demonstrating rapid and/or non-gradual evolutionary change. What do these exceptions to slow and gradual rates of evolution tell us about higher-level processes? How does such episodic change fit into our neo-Darwinian notions of gradualism? And what exciting prospects do we expect to discover in the future?