This well-written book provides an excellent explanation for how a toolkit of genes like the Hox genes (see image below) control embryonic development in animals. The discovery of these genes shows that fruit flies, starfish, and people are more closely related than was once believed.
These genes work by producing proteins which in turn control the expression of other genes, in what is effectively a kind of computer program that can be visualised (and Endless Forms Most Beautiful contains several lovely colour plates which confirm this).
Carroll concludes with a plea for teaching more evolutionary biology in schools. Personally, I think a greater priority would be an increased emphasis on teaching ecology, given the serious consequences which human activities (even well-meaning ones) can have for the planet. However, that quibble does not stop me from recommending this book to anyone who has not read it yet.
Endless Forms Most Beautiful by Sean B. Carroll: 3½ stars
Drosophila melanogaster, the vinegar fly or “fruit fly” (photo above by André Karwath), has been enormously important as a model organism in genetics and neuroscience, partly because it is so easy to raise in the laboratory (photo below by “Masur”).
Drosophila genes such as fruitless, rutabaga, and white have been enormously important within biology, and flybase.org provides a modern repository of information on such genes. The Hox genes, first found in Drosophila (see below), form part of the complex machinery of embryonic development, which allows protein synthesis to be controlled in both time and space.
Jonathan Weiner’s 1999 book Time, Love, Memory is one of a number of books which explain how valuable this little insect has been.
Arabidopsis thaliana, the thale cress (photo above by Peggy Greb, picture below by Johann Georg Sturm and Jacob Sturm, 1796) is a small flowering plant in the family Brassicaceae – the mustard/cabbage family.
During much of the 20th century, A. thaliana was the target of extensive research, facilitated by the small size of the plant (and of its genome), its short life cycle, and its suitability for light microscopy. Sequencing of the genome was completed in the year 2000, and the genome is available at arabidopsis.org. The open-access peer-reviewed The Arabidopsis Book also collates information on the plant, which is in many ways the botanical equivalent of Caenorhabditis elegans. It has taught the world a great deal.
The Google Ngram below shows the explosion in Arabidopsis-related literature since about 1990, outstripping even work on C. elegans:
Caenorhabditis elegans (photo above by Bob Goldstein, diagram below by “KDS444”) is a transparent nematode worm, about 1 mm in length. It lives naturally in the soil, where it eats bacteria, but it is also quite happy to make its home in a Petri dish. A 1963 suggestion by Sydney Brenner led to C. elegans becoming the focal point of a vast collaborative effort to understand the worm in detail. Brenner shared the 2002 Nobel Prize in Physiology or Medicine for this work.
The cellular development of C. elegans has been mapped in detail, and its genome had been largely mapped by 1998. The diagram below shows the neural network of the worm, drawn using R, based on data from here (from this paper via this one). In this diagram, colour shows the centrality of neurons in the network. Other information on C. elegans is available at wormbase.org.
Because of the effort that has gone into understanding this humble worm as whole, rather than as just parts, a great deal has been learned about biology in general. Brenner was on to a good thing!
Salichos and Rokas point out the inconsistent phylogenetic trees produced by different DNA studies. There is disagreement, for example, on whether gastropods (left, below) are more closely related to bivalves (centre) or scaphopods (right).
Image on right by Hans Hillewaert, others public domain
Gastropods are grouped with scaphopods here, for example, but scaphopods with bivalves here.
While Salichos and Rokas give some answers, part of the problem, in my view, is the common tendency to use maximum-likelihood methods to produce a single phylogenetic tree. The standard algorithms will always produce such a tree of course, but it is important to give the equivalent of error bars, and indicate the range of possible trees supported by a given dataset. Phylogenetic networks, like the one below, are a way of doing this. Occasionally, the data forces us to say “we’re not quite sure” to some questions that have been asked.
Phylogenetic network by Katharina M. Jörger, Isabella Stöger, Yasunori Kano, Hiroshi Fukuda, Thomas Knebelsberger, and Michael Schrödl (see their paper)