Phylogenetic networks are fundamentally different from other kinds of biological networks

Abstract

Complex networks are found in all parts of biology, but there are at least two distinct types of biological networks. In the most common type, the nodes and edges are empirically observed, and the network analysis involves summarizing the characteristics of the network. In the second type, only the leaf nodes are observed, and the internal nodes and all of the edges must be inferred from information available about the leaf nodes. Perhaps the most widespread of this inferred type of network is the phylogenetic network, which illustrates the genealogical history and the connection of all life. Evolution involves a series of unobservable historical events, each of which is unique, and we can neither make direct observations of them nor perform experiments to investigate them. This makes a phylogenetic study one of the hardest forms of data analysis known, as there is no mathematical algorithm for discovering unique historical accidents. This chapter summarizes the essential differences of this network type and discusses the consequences of these differences. Due to the complexity of evolutionary history, two types of phylogenetic networks have been developed, which have been actively used in parallel by biologists for 150 years: (1) rooted evolutionary networks, in which the internal nodes represent ancestors of the leaf nodes, and the directed edges represent historical pathways of transfer of genetic information between ancestors and their descendants; and (2) unrooted datadisplay networks, in which the internal nodes do not represent ancestors, and the undirected edges represent affinity (e.g. similarity) relationships among the leaf nodes. The latter type of network is the most commonly encountered in phylogenetics, because there is a wide range of available mathematical techniques that work well. They have been put to a number of uses by phylogeneticists, including exploratory data analysis, displaying similarity patterns, displaying data conflicts, summarizing analysis results, and testing phylogenetic hypotheses; and I illustrate each of these with an empirical example. There are, as of yet, few mathematical techniques available for evolutionary networks, and recent focus has therefore been on the development of practical and effective methods. There are, however, a wide range of methodological questions that need to be answered before this can happen; and I raise a number of these here, along with a preliminary discussion of them. There are also issues related to the realism of the common mathematical constraints, the evolutionary units in a network, and the concept of a most recent common ancestor. © 2013 by Nova Science Publishers, Inc. All rights reserved.

Keywords

Evolution; Evolutionary networks; Exploratory data analysis; Genealogical relationships: Phylogenetic networks; Phylogenetics

Published in

Title: Network biology : theories, methods and applications
Publisher: Nova Science Publishers, Inc.

SLU Authors

• Morrison, David

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Evolutionary Biology

Publication identifier

• ISBN: 9781626189423

Permanent link to this page (URI)

https://res.slu.se/id/publ/132178