Introduction

The complexity of cells continues to fascinate scientists. Two broad views are often advanced to account for such complexity. In one, it is assumed that any complexity must necessarily benefit the cell. Some cell and molecular biologists go even further and discuss how a particular mechanism was “designed” by evolution to be perfectly matched to its task. As with a machine, it is assumed that every molecular nut and bolt must have a purpose. Because this view seems intuitive and relatively simple (after all, examples abound of animals, plants, and microbes adapted to their environments), it is often invoked to explain any aspect of cell and molecular biology. A different view, the one we elaborate here, is embodied in Dobzhansky’s famous line, now a cliché, “nothing in biology makes sense except in the light of evolution.” According to this view, any rationalization of a modern cellular mechanism depends critically on understanding its evolutionary history. We argue that this emphasis on evolutionary history is especially appropriate for analyzing transcription circuits and for rationalizing their structures. This view has explanatory power in that it can readily account for some of the more bewildering and counterintuitive features of modern transcription circuits; it also gives us insight into the best ways to describe and study such circuits.

In this Perspective, we first review common features of transcription network structures—observed across diverse species—and argue that these similarities cannot be the result of descent from a single ancestral circuit possessing these characteristics. Next, we consider key biochemical and biophysical properties of transcription regulators and cis-regulatory sequences that make certain evolutionary pathways much more probable than others, in part because they circumvent fitness barriers. Finally, we argue that many aspects of transcription circuits, particularly those that seem overly complex and counterintuitive, can be understood as relatively crude products of high-probability evolutionary trajectories rather than as highly optimized, specific solutions.