Scientists have long known what drives the flagellum to spin, but what causes the flagellum to stop spinning — temporarily or permanently — was unknown.

“We think it’s pretty cool that evolving bacteria and human engineers arrived at a similar solution to the same problem,” said IU Bloomington biologist Daniel Kearns, who led the project. “How do you temporarily stop a motor once it gets going?”

The action of the protein they discovered, EpsE, is very similar to that of a car clutch. In cars, the clutch controls whether a car’s engine is connected to the parts that spin its wheels.

-ScienceDaily (June 23, 2008)

Excerpts from The Design Matrix (2007):

We are actually using the knowledge about our own designed artifacts to shed light on biology and how it works. Imagine if there were no computers, tape recorders, and washing machines. Take away human technology, and suddenly, there are no good metaphors left to describe life. - p 52

All of this, of course, makes sense if life really is carbon-based nanotechnology. To understand an alien technology, we would have to use our own technology as a model, and the more similar the technologies, the more easily we could characterize and understand this alien technology. - p 53

If living processes are the products of design, it comes as no surprise that so much of biology is more akin to the study of engineering than to chemistry or physics. Furthermore, it would make sense that as our understanding of the cell advances, that teleological concepts, including the very concept of design, would proliferate in the biological literature. Much of this design terminology stems from the fact that biologists have discovered that cells are filled with miniature machines and that coded information is stored and employed for the synthesis of these machines.- p. 59

Looking at a cell is like looking into the future of our own designs. – p. 204

Embedded within cells are complex signaling mechanisms that transfer information from one part of a cell to another and intercellular mechanisms that transfer information from one part of a multicellular organism to another. Indeed, signal transduction pathways—and the proteins associated with them—appear to serve the functions of information processing and transfer, rather than those of more “traditional” biology (e.g., chemical transformation of metabolic intermediates or the building of cellular structures).

For example, a simple enzyme protein could be viewed as a computational element that takes an input—the concentration of its “substrate,” the molecule with which it interacts—and produces an output: a concentration of the catalyzed reaction product. An enzyme that becomes active only when it binds to two separate regulator molecules will function something like a Boolean AND gate, and so on. Circuits formed from these elements can be as simple as a switch or an oscillator, or as complex as to drive a bacterium’s chemotaxis response. Indeed, the cell even possesses a kind of short-term, “random-access” memory, in the sense that events in its environment have profoundly shaped the concentration and activity of many thousands of molecules in the cell. In short, these protein-based circuits constitute a kind of nervous system for the cell, providing it with much of what it needs to control its behavior.

Additional insights can be gained from the notion that both computational processes and biological pathways can be viewed as processes that affect the state of a system according to well-defined (though possibly probabilistic) rules. Thus, it is possible to describe regulatory, metabolic, and signaling pathways, as well as multicellular processes such as immune responses, as systems of interacting computations operating in parallel. In particular, languages such as Petrinets, Statecharts, and the Pi-calculus, originally developed for the specification and study of systems of interacting computations, can be used to represent such systems. Such representations enable researchers to simulate their behavior, and to support qualitative and quantitative reasoning on the properties of these systems.

- From
Catalyzing Inquiry at the Interface of Computing and Biology, pp. 207-208