Untangling the Logic of Gene Circuits

How does a cell process information? Unlike computers, with CPUs to carry out calculations, and animals, which have brains that process sensory information, cells have no centralized device for processing the many internal and external signals with which they are constantly bombarded. And yet they somehow manage just fine. The single-celled brewers's yeast, for example, can know what kind of food source is available, tell when it's hot or cold, and even find a mate.

One key way that cells sense and respond to their environment is via genetic circuits. Although biologists often use the word 'circuit' in a sense that is only loosely analogous to electrical circuits, recent research is putting our understanding of genetic circuits on a much more rigorous and quantitative footing. By studying very simple circuits, using computer models and simple experiments, we are starting to understand, in a still very limited way, why the cell is wired up the way it is.

Let's take an example of a simple of a wiring setup that occurs very commonly in gene regulation. Suppose that gene A turns on gene B. (Technically, gene A does not turn on anything - gene A directs the synthesis of protein A, which can then turn on gene B, but when we talk about genetic networks, this is taken for granted.) A also turns on another gene, C. Gene B turns on gene C as well, so you get a little system wired up like this:



Initially, this configuration, called a feed forward loop may not make much sense. If gene C is turned on by A, then why do you need B? The key to this whole setup is that C requires both A and B to be fully on. If gene C needs both A and B in order to be switched on, we now have a circuit that is resistant to noise.


To see how this works, let's view this from the perspective of a small bacterium, such as E. coli. An individual bacterium is constantly in search of food; it can only swim around so long before it runs out of energy. E. coli can use a variety of different food sources, but it needs to turn on the proper genes for each food. When the sugar arabinose is available, E. coli switches on the genes that enable it to import and metabolize arabinose. But turning on the whole suite of arabinose genes requires some effort; it's important that the bacterium not go through all that effort only to find out that there is no arabinose around after all.

Going back to our little circuit, let's suppose that A is sensitive to arabinose. When arabinose is around, A turns on B, and A and B turn on C; gene C makes an enzyme that can help metabolize arabinose. But A could get turned on by just a trace of arabinose; this kind of random noise would be disastrous if A was always switching on C at the slightest provocation. We only want C around when there is a seriously good arabinose source.

Enter the feed forward loop - it filters out the noise! It works like this:

Scenario 1 - random noise, or just a trace of arabinose:

1. A gets turned on briefly, and then shuts off.

2. B barely gets switched on by A, but not enough to affect C.

3. C never gets turned on.



Scenario 2 - sustained arabinose signal:

1. A gets turned on, reaches a maximal level and stays on for a period.

2. B gets switched on by A and hits its maximal level.

3. C gets turned on once A and B reach their maximal levels.

4. The bacterium metabolizes arabinose.



Such genetic circuits are extremely common in biology, although most often they occur in much more complex combinations than I've shown here. One current idea is that the more complex combinations are built up out of simple circuits like this Feed Forward Loop, and the hope is that we can use our understanding of these simple circuits to make sense of the information processing properties of the massively tangled networks that we find in all cells. This is still mainly just a hope though; although there are some increasingly sophisticated computer models of complex genetic networks, there is precious little experimental work demonstrating that we have actually learned something about these complex networks.

The experimental situation is different though for simple networks - several research groups have carried out some very nice experiments on simple systems. Uri Alon is one of the leaders in this field (and my figures are redrawn from his recent review of this field.) His group has performed experiments to test the effects of these simple genetic circuits, and other groups are doing similar studies.

So, while a useful, rigorous, experiment-based understanding of more complex networks is still just a hope, our understanding of small, functional circuits is enabling us to delve deeper into the information processing properties of the cell.