5 Metazoan Complexity
169
and why certain levels of developmental programmes seem to tolerate more changes
than others. As the ultimate functional “output” of these programmes, the swimming
tadpole, appears to be remarkably stable, the answers to these questions probably
also provide insights into the molecular correlates of morphological and cellular
complexity.
5.14 Perspectives
As the examples covered in this chapter show, the study of new marine model
systems adds new and interesting perspectives to our understanding of animal complexity. As a complement to traditional characteristics of complexity, such as the
number of differentiated cell types, genomic approaches offer different measures
of complexity. On a basic level, these are the number of protein domains, genes,
introns and transcripts in a given organism that underlie its genetic networks. Such
features are straightforward to compare, and have helped to date the existence of
many domains, genes and introns back to more basal positions in diverse animal
groups. These, as well as additional findings, have several conceptual implications.
Loss of Ancestral Complexity as an Evolutionary Principle: The study of new
model systems recurrently finds evidence for features that were present in ancestral
genomes, yet were secondarily lost along certain evolutionary lineages. This helps
to readjust our view on animal evolution: apparently, loss of ancestral features is
part of the normal evolutionary process, just as gene duplication and modification
are plausible sources of new genomic complexity. The genes in question include
regulatory genes such as transcription factors, but also extracellular signals, factors
that – from functional analyses – are known to have significant impact on animal
development. It is therefore an intriguing question as to the role the apparent loss of
ancestral complexity has played in shaping the evolutionary process.
Differences in evolutionary speed: In addition to the existence of loss as a fundamental principle of evolution, we note that there are pronounced differences between
animal groups with respect to the extent of losses they display. Examples of this are
the slow evolutionary pace of annelids or cephalochordates, as opposed to the massive acceleration in ascidians. As these marine examples illustrate, the question of
fast vs. slow evolution is not coupled with whether or not a group occurs in the sea.
The new focus on marine species, however, helps to systematically fill important
gaps in the phylogeny of animals that contributes to our understanding of what are
ancestral, and what are secondarily acquired features.
Regulatory Networks: Much of the focus of modern genomic approaches has
been on the comparison of simple features, such as the presence or absence of genes
and introns. As discussed in the introduction, however, this level is only a first measure of complexity. How do the single entities work together to form regulatory
networks, and how does complexity on the level of network components impact on
the complexity of the resulting networks themselves? Questions of this type require
a combination of functional tools like gene interference coupled with expression
169
and why certain levels of developmental programmes seem to tolerate more changes
than others. As the ultimate functional “output” of these programmes, the swimming
tadpole, appears to be remarkably stable, the answers to these questions probably
also provide insights into the molecular correlates of morphological and cellular
complexity.
5.14 Perspectives
As the examples covered in this chapter show, the study of new marine model
systems adds new and interesting perspectives to our understanding of animal complexity. As a complement to traditional characteristics of complexity, such as the
number of differentiated cell types, genomic approaches offer different measures
of complexity. On a basic level, these are the number of protein domains, genes,
introns and transcripts in a given organism that underlie its genetic networks. Such
features are straightforward to compare, and have helped to date the existence of
many domains, genes and introns back to more basal positions in diverse animal
groups. These, as well as additional findings, have several conceptual implications.
Loss of Ancestral Complexity as an Evolutionary Principle: The study of new
model systems recurrently finds evidence for features that were present in ancestral
genomes, yet were secondarily lost along certain evolutionary lineages. This helps
to readjust our view on animal evolution: apparently, loss of ancestral features is
part of the normal evolutionary process, just as gene duplication and modification
are plausible sources of new genomic complexity. The genes in question include
regulatory genes such as transcription factors, but also extracellular signals, factors
that – from functional analyses – are known to have significant impact on animal
development. It is therefore an intriguing question as to the role the apparent loss of
ancestral complexity has played in shaping the evolutionary process.
Differences in evolutionary speed: In addition to the existence of loss as a fundamental principle of evolution, we note that there are pronounced differences between
animal groups with respect to the extent of losses they display. Examples of this are
the slow evolutionary pace of annelids or cephalochordates, as opposed to the massive acceleration in ascidians. As these marine examples illustrate, the question of
fast vs. slow evolution is not coupled with whether or not a group occurs in the sea.
The new focus on marine species, however, helps to systematically fill important
gaps in the phylogeny of animals that contributes to our understanding of what are
ancestral, and what are secondarily acquired features.
Regulatory Networks: Much of the focus of modern genomic approaches has
been on the comparison of simple features, such as the presence or absence of genes
and introns. As discussed in the introduction, however, this level is only a first measure of complexity. How do the single entities work together to form regulatory
networks, and how does complexity on the level of network components impact on
the complexity of the resulting networks themselves? Questions of this type require
a combination of functional tools like gene interference coupled with expression