11. PRESENT AND FUTURE SCENARIO

11. PRESENT AND FUTURE SCENARIO

In 2000, Gardner and his colleagues James Collins and Charles Cantor, both also of Boston University, built a memory device in E. coli out of two inverters for which the output protein of one is the input protein of the other, and vice versa. In the same year, Michael Elowitz and Stanislas Leibler of Rockefeller University in New York City made an oscillator in which three inverters are connected in a loop so that the output protein of each inverter is the input protein of the next inverter. In one test of their system, a fluorescent protein became active whenever one of the proteins was in its low state. The result was a population of gently twinkling cells like flashing holiday lights, Elowitz says. "It was very beautiful," he says.

Weiss' team has just put the finishing touches on a five-gene circuit in E. coli that can detect a particular chemical in its surroundings and turn on a fluorescent protein when the chemical concentration falls within preselected bounds. Such circuits could eventually be used to detect environmental toxins, Weiss notes. Ultimately, he says, different cells could be programmed to respond to different concentrations of a toxic chemical and to fluoresce in different colors, so that the cell population would generate a color-coded topographical map of the toxin concentrations.

A. Bioware: All of the molecular computing methods mentioned above envision that the computation will be done in vitro. Although the molecules are of biological origin, they are extracted from the cell, and the reaction takes place in laboratory glassware. But why not turn the living cell itself into a computer, powered by its own metabolism? Several research collaborations have done work pointing toward this possibility. The following are the ideas of a group at MIT, who have examined the computational aspects of the problem in great detail. The MIT group consists of Thomas F. Knight, Jr., Harold Abelson and Gerald Jay Sussman, and several of their present and former students, including Don Allen, Daniel Coore, Chris Hanson, George E. Homsy, Radhika Nagpal, Erik Rauch and Ron Weiss.The first major goal of the MIT group is to develop design rules and a parts catalogue for biological computers, like the comparable tools that facilitate design of electronic integrated circuits. An engineer planning the layout of a silicon chip does not have to define the geometry of each transistor individually; those details are specified in a library of functional units, so that the designer can think in terms of higher-level abstractions such as logic gates and registers. A similar design discipline will be needed before biocomputing can become practical.

The elements of the MIT biocomputing design library will be repressor proteins. The logic “family” might be named RRL, for repressor-repressor logic, in analogy with the long-established TTL, which stands for transistor-transistor logic. The basic not gate in RRL will be a gene encoding some repressor protein (call it Y), with transcription of the Y gene regulated in turn by a different repressor (call it X). Thus whenever X is present in the cell, it binds near the promoter site for Y and blocks the progress of RNA polymerase. When X is absent, transcription of Y proceeds normally. Because the Y protein is itself a repressor, it can serve as the input to some other logic gate, controlling the production of yet another repressor protein, say Z. In this way gates can be linked together in a chain or cascade.

Going beyond the not gate to other logical operations calls for just a little more complexity. Inserting binding sites for two repressor proteins (A and B) upstream of a gene for protein C creates a nand gate, which computes the logical function not and. With the dual repressor sites in place, the C gene is transcribed only if both A and are absent from the cell; if either one of them should rise above a threshold level, production of C stops. It is a well-known result in mathematical logic that with enough nand and not gates, you can generate any Boolean function you please. For example, the function (A or B) is equivalent to (not (A nand B)), while (A and B) is ((not A) nand (not B)). The not gate itself can be viewed as just a degenerate nand with only one input. Thus with no more resources than a bunch of nand gates, you can build any logical network.

Figure 5 Design of a biochemical nand logic gate connected to a downstream inverter. The two-input nand gate consists of two separate inverters, each with a different input, but both with the same output protein. The nand gate output is always high unless both inputs are present. This output can then be connected to other downstream gates, such as an inverter.

Pairs of nand gates can also be coupled together to form the computer memory element known as a flip-flop, or latch. Implementing this concept in RRL calls for two copies of the genes coding for two repressor proteins, M and N. One copy of the M gene is controlled by a different repressor, R, and likewise one copy of the N gene is regulated by repressor S. The tricky part comes in the control arrangements for the second pair of genes: Here the repressor of M is protein N, and symmetrically the repressor of N is M. In other words, each of these proteins inhibits the other’s synthesis. Here’s how the flip-flop works. Suppose initially that both R and S are present in the cell, shutting down both of the genes in the first pair; but protein M is being made at high levels by the M gene in the second pair. Through the cross coupling of the second pair, M suppresses the output of N, with the collateral result that M’s own repressor site remains vacant, so that production of M can continue. But now imagine that the S protein momentarily falls below threshold. This event briefly lifts the repression of the N gene in the first pair. The resulting pulse of N protein represses the M gene in the second pair, lowering the concentration of protein M, which allows a little more N to be manufactured by the second N gene, which further inhibits the second M gene, and so on. Thus a momentary change in S switches the system from steady production of M to steady production of N. Likewise a brief blip in R would switch it back again. (S and R stand for “set” and “reset.”)

One conclusion to be drawn from this synopsis of a few RRL devices is that a computer based on genetic circuits will need a sizable repertory of different repressor proteins. Each logic gate inside a cell must have a distinct repressor assigned to it, or else the gates would interfere with one another. In this respect a biomolecular computer is very different from an electronic one, where all signals are carried by the same medium—an electric current. The reason for the difference is that electronic signals are steered by the pattern of conductors on the surface of the chip, so that they reach only their intended target. The biological computer is a wireless device, where signals are broadcast throughout the cell. The need to find a separate repressor for every signal complicates the designer’s task, but there is also a compensating benefit. On electronic chips, communication pathways claim a major share of the real estate. In a biochemical computer, communication comes for free.

Are there enough repressor proteins available to create useful computational machinery? Note that interference between logic gates is not the only potential problem; the repressor molecules taking part in the computation must also be distinct from those involved in the normal metabolism of the cell. Otherwise, a physiological upset could lead to a wrong answer; or, conversely, a computation might well poison the cell in which it is running. A toxic instruction might actually be useful—any multitasking computer must occasionally “kill” a process—but unintended events of this kind would be a debugging nightmare. You can’t just reboot a dead bacterium.
Nature faces the same problem: A multitude of metabolic pathways have to be kept under control without unwanted cross talk. As a result, cells have evolved thousands of distinct regulatory proteins. Moreover, the Biocomputing engineer will be able to mix and match among molecules and binding sites that may never occur together in the natural world. The aim of the RRL design rules is to identify a set of genes and proteins that can be encapsulated as black-box components, to be plugged in as needed without any thought about conflicts.