In particular, when a lymphocyte is born, a novel set of identical receptors is created via a complicated random shuffling process in the lymphocyte's DNA. Because of continual turnover of the lymphocyte population (about ten million new lymphocytes are born each day), the body is continually introducing lymphocytes with novel receptor shapes. For any pathogen that enters the body, it will just be a short time before the body produces a lymphocyte that binds to that pathogen's particular marker molecules (i.e., antigens), though the binding might be fairly weak.

Once such a binding event takes place, the immune system has to figure out whether it is indicative of a real threat or is just a nonthreatening situation that can be ignored. Pathogens are harmful, of course, because once they enter the body they start to make copies of themselves in large numbers. However, launching an immune system attack can cause inflammation and other harm to the body, and too strong an attack can be lethal. The immune system as a whole has to determine whether the threat is real and severe enough to warrant the risk of an immune response harming the body. The immune system will go into high-gear attack mode only if it starts picking up a lot of sufficiently strong binding events.

The two types of lymphocytes, B and T cells, work together to determine whether an attack is warranted. If the number of strongly bound receptors on a B cell exceeds some threshold, and in the same time frame the B cell gets "go-ahead" signals from T cells with similarly bound receptors, the B cell is activated, meaning that it now perceives a threat to the body (figure 12.3). Once activated, the B cell releases antibody molecules into the bloodstream. These antibodies bind to antigens, neutralize them, and mark them for destruction by other immune system cells.

The activated B cell then migrates to a lymph node, where it divides rapidly, producing large numbers of daughter cells, many with mutations that alter the copies' receptor shapes. These copies are then tested on antigens that are captured in the lymph node. The cells that do not bind die after a short time.

FIGURE 12.3. Ill.u.s.tration of activation of a B cell via binding and "go-ahead" signal from a T cell. This signal prompts the B cell to produce and release antibodies (y-shaped molecules).

The surviving copies are unleashed into the bloodstream, where some of them encounter and bind to antigens, in some cases more strongly than did their mother B cell. These activated daughter B cells also migrate to lymph nodes and create mutated daughters of their own. This ma.s.sive production of lymphocytes is one reason that your lymph nodes often feel swollen when you are sick.

This cycle continues, with the new B cells that best match antigens themselves producing the most daughter cells. In short, this is a process of natural selection, in which collections of B cells evolve to have receptor shapes that will bind strongly to a target antigen. This results in a growing a.r.s.enal of antibodies that have been "designed" via selection to attack this specific antigen. This process of detection and destruction typically takes from a few days to weeks to eradicate the corresponding pathogen from the body.

There are at least two potential problems with this strategy. First, how does the immune system prevent lymphocytes from mistakenly attacking the body's own molecules? Second, how does the immune system stop or tone down its attack if the body is being harmed too much as a result?

Immunologists don't yet have complete answers to these questions, and each is currently an area of active research. It is thought that one major mechanism for avoiding attacking one's own body is a process called negative selection. When lymphocytes are born they are not immediately released into the bloodstream. Instead they are tested in the bone marrow and thymus by being exposed to molecules of one's own body. Lymphocytes that bind strongly to "self" molecules tend to be killed off or undergo "editing" in the genes that give rise to receptors. The idea is that the immune system should only use lymphocytes that will not attack the body. This mechanism often fails, sometimes producing autoimmune disorders such as diabetes or rheumatoid arthritis.

A second major mechanism for avoiding autoimmune attacks seems to be the actions of a special subpopulation of T cells called regulatory T cells. It's not yet known exactly how these regulatory T cells work, but they do secrete chemicals that suppress the actions of other T cells. A third mechanism has been hypothesized to be the compet.i.tion among B cells for a limited resource-a particular chemical named BAFF needed for B cells to survive. B cells that slip through the negative selection process and still bind strongly to self-molecules find themselves, due to their continual binding to self-molecules, in need of higher amounts of BAFF than non-self-binding B cells. Compet.i.tion for this limited resource leads to the increased probability of death for self-binding B cells.

Even if the immune system is attacking foreign pathogens, it needs to balance the virulence of its attack with the obligation to prevent harm to the body as much as possible. The immune system employs a number of (mostly little understood) mechanisms for achieving this balance. Many of these mechanisms rely on a set of signaling molecules called cytokines. Harm to the body can result in the secretion of cytokines, which suppress active lymphocytes. Presumably the more harm being done, the higher the concentration of suppression cytokines, which makes it more likely that active cells will encounter them and turn off, thus regulating the immune system without suppressing it altogether.

Ant Colonies.

As I described in chapter 1, a.n.a.logies often have been made between ant colonies and the brain. Both can be thought of as networks of relatively simple elements (neurons, ants) from which emerge larger-scale information-processing behaviors. Two examples of such behavior in ant colonies are the ability to optimally and adaptively forage for food, and the ability to adaptively allocate ants to different tasks as needed by the colony. Both types of behavior are accomplished with no central control, via mechanisms that are surprisingly similar to those described above for the immune system.

In many ant species, foraging for food works roughly as follows. Foraging ants in a colony set out moving randomly in different directions. When an ant encounters a food source, it returns to the nest, leaving a trail made up of a type of signaling chemicals called pheromones. When other ants encounter a pheromone trail, they are likely to follow it. The greater the concentration of pheromone, the more likely an ant will be to follow the trail. If an ant encounters the food source, it returns to the nest, reinforcing the trail. In the absence of reinforcement, a pheromone trail will evaporate. In this way, ants collectively build up and communicate information about the locations and quality of different food sources, and this information adapts to changes in these environmental conditions. At any given time, the existing trails and their strengths form a good model of the food environment discovered collectively by the foragers (figure 12.4).

Task allocation is another way in which an ant colony regulates its own behavior in a decentralized way. The ecologist Deborah Gordon has studied task allocation in colonies of Red Harvester ants. Workers in these colonies divide themselves among four types of tasks: foraging, nest-maintenance, patrolling, and refuse-sorting work. The number of workers pursuing each type of task adapts to changes in the environment. Gordon found, for example, that if the nest is disturbed in some small way, the number of nest maintenance workers will increase. Likewise, if the food supply in the neighborhood is large and high quality, the number of foragers will increase. How does an individual ant decide which task to adopt in response to nestwide environmental conditions, even though no ant directs the decision of any other ant and each ant interacts only with a small number of other ants?

FIGURE 12.4. An ant trail. (Photograph copyright by Flagstaffotos. Reproduced by permission.) The answer seems to be that ants decide to switch tasks both as a function of what they encounter in the environment and as a function of the rate at which they encounter other ants performing different tasks. For example, an inactive ant-one not currently performing a task-that encounters a foreign object near the nest has increased probability of taking up nest-maintenance work. In addition, an inactive ant that encounters a high rate of nest-maintenance workers entering and leaving the nest will also have an increased probability of adopting the nest-maintenance task; the increased activity in some way signals that there are important nest maintenance tasks to be done. In a similar way, a nest-maintenance worker who encounters a high rate of foragers returning to the nest carrying seeds will have an increased probability of switching to foraging; the increased seed delivery signals in some way that a high-quality food source has been found and needs to be exploited. Ants are apparently able to sense, through direct contact of their antennae with other ants, what task the other ants have been engaged in, by perceiving specific chemical residues a.s.sociated with each task.

Similar types of mechanisms-based on pheromone signals and direct interaction among individuals-seem to be responsible for other types of collective behavior in ants and other social insects, such as the construction of bridges or shelters formed of ants' bodies described in chapter 1, although many aspects of such behavior are still not very well understood.

Biological Metabolism.

Metabolism is the group of chemical processes by which living organisms use the energy they take in from food, air, or sunlight to maintain all the functions needed for life. These chemical processes occur largely inside of cells, via chains of chemical reactions called metabolic pathways. In every cell of an organism's body, nutrient molecules are processed to yield energy, and cellular components are built up via parallel metabolic pathways. These components are needed for internal maintenance and repair and for external functions and intercellular communication. At any given time, millions of molecules in the cell drift around randomly in the cytoplasm. The molecules continually encounter one another. Occasionally (on a scale of microseconds), enzymes encounter molecules of matching shape, speeding up the chemical reactions the enzymes control. Sequences of such reactions cause large molecules to be built up gradually.

Just as lymphocytes affect immune system dynamics by releasing cytokines, and as ants affect foraging behavior by releasing pheromones, chemical reactions that occur along a metabolic pathway continually change the speed of and resources given to that particular pathway.

In general, metabolic pathways are complex sequences of chemical reactions, controlled by self-regulating feedback. Glycolysis is one example of a metabolic pathway that occurs in all life forms-it is a multistep process in which glucose is transformed into the chemical pryruvate, which is then used by the metabolic pathway called the citric acid cycle to produce, among other things, the molecule called ATP (adenosine triphosphate), which is the princ.i.p.al source of usable energy in a cell.

At any given time, hundreds of such pathways are being followed, some independent, some interdependent. The pathways result in new molecules, initiation of other metabolic pathways, and the regulation of themselves or other metabolic pathways.

Similar to the regulation mechanisms I described above for the immune system and ant colonies, metabolic regulation mechanisms are based on feedback. Glycolysis is a great example of this. One of the main purposes of glycolysis is to provide chemicals necessary for the creation of ATP. If there is a large amount of ATP in the cell, this slows down the rate of glycolysis and thus decreases the rate of new ATP production. Conversely, when the cell is lacking in ATP, the rate of glycolysis goes up. In general, the speed of a metabolic pathway is often regulated by the chemicals that are produced by that pathway.

Information Processing in These Systems.

Let me now attempt to answer the questions about information processing I posed at the beginning of this chapter: What plays the role of "information" in these systems?

How is it communicated and processed?

How does this information acquire meaning? And to whom?

WHAT PLAYS THE ROLE OF INFORMATION?.

As was the case for cellular automata, when I talk about information processing in these systems I am referring not to the actions of individual components such as cells, ants, or enzymes, but to the collective actions of large groups of these components. Framed in this way, information is not, as in a traditional computer, precisely or statically located in any particular place in the system. Instead, it takes the form of statistics and dynamics of patterns over the system's components.

In the immune system the spatial distribution and temporal dynamics of lymphocytes can be interpreted as a dynamic representation of information about the continually changing population of pathogens in the body. Similarly, the spatial distribution and dynamics of cytokine concentrations encode large-scale information about the immune system's success in killing pathogens and avoiding harm to the body.

In ant colonies, information about the colony's food environment is represented, in a dynamic way, by the statistical distribution of ants on various trails. The colony's overall state is represented by the dynamic distribution of ants performing different tasks.

In cellular metabolism information about the current state and needs of the cell are continually reflected in the spatial concentrations and dynamics of different kinds of molecules.

HOW IS INFORMATION COMMUNICATED AND PROCESSED?.

Communication via Sampling.

One consequence of encoding information as statistical and time-varying patterns of low-level components is that no individual component of the system can perceive or communicate the "big picture" of the state of the system. Instead, information must be communicated via spatial and temporal sampling.

In the immune system, for example, lymphocytes sample their environment via receptors for both antigens and signals from other immune system cells in the form of cytokines. It is the results of the lymphocytes' samples of the spatial and temporal concentration of these molecular signals that cause lymphocytes to become active or stay dormant. Other cells are in turn affected by the samples they take of the concentration and type of active lymphocytes, which can lead pathogen-killer cells to particular areas in the body.

In ant colonies, an individual ant samples pheromone signals via its receptors. It bases its decisions on which way to move on the results of these sampled patterns of concentrations of pheromones in its environment. As I described above, individual ants also use sampling of concentration-based information-via random encounters with other ants-to decide when to adopt a particular task. In cellular metabolism, feedback in metabolic pathways arises from bindings between enzymes and particular molecules as enzymes sample spatial and time-varying concentrations of molecules.

Random Components of Behavior.

Given the statistical nature of the information read, the actions of the system need to have random (or at least "unpredictable") components. All three systems described above use randomness and probabilities in essential ways. The receptor shape of each individual lymphocyte has a randomly generated component so as to allow sampling of many possible shapes. The spatial pattern of lymphocytes in the body has a random component due to the distribution of lymphocytes by the bloodstream so as to allow sampling of many possible spatial patterns of antigens. The detailed thresholds for activation of lymphocytes, their actual division rates, and the mutations produced in the offspring all involve random aspects.

Similarly, the movements of ant foragers have random components, and these foragers encounter and are attracted to pheromone trails in a probabilistic way. Ants also task-switch in a probabilistic manner. Biochemist Edward Ziff and science historian Israel Rosenfield describe this reliance on randomness as follows: "Eventually, the ants will have established a detailed map of paths to food sources. An observer might think that the ants are using a map supplied by an intelligent designer of food distribution. However, what appears to be a carefully laid out mapping of pathways to food supplies is really just a consequence of a series of random searches."

Cellular metabolism relies on random diffusion of molecules and on probabilistic encounters between molecules, with probabilities changing as relative concentrations change in response to activity in the system.

It appears that such intrinsic random and probabilistic elements are needed in order for a comparatively small population of simple components (ants, cells, molecules) to explore an enormously larger s.p.a.ce of possibilities, particularly when the information to be gained from such explorations is statistical in nature and there is little a priori knowledge about what will be encountered.

However, randomness must be balanced with determinism: self-regulation in complex adaptive systems continually adjusts probabilities of where the components should move, what actions they should take, and, as a result, how deeply to explore particular pathways in these large s.p.a.ces.

Fine-Grained Exploration.

Many, if not all, complex systems in biology have a fine-grained architecture, in that they consist of large numbers of relatively simple elements that work together in a highly parallel fashion.

Several possible advantages are conferred by this type of architecture, including robustness, efficiency, and evolvability. One additional major advantage is that a fine-grained parallel system is able to carry out what Douglas Hofstadter has called a "parallel terraced scan." This refers to a simultaneous exploration of many possibilities or pathways, in which the resources given to each exploration at a given time depend on the perceived success of that exploration at that time. The search is parallel in that many different possibilities are explored simultaneously, but is "terraced" in that not all possibilities are explored at the same speeds or to the same depth. Information is used as it is gained to continually rea.s.sess what is important to explore.

For example, at any given time, the immune system must determine which regions of the huge s.p.a.ce of possible pathogen shapes should be explored by lymphocytes. Each of the trillions of lymphocytes in the body at any given time can be seen as a particular mini-exploration of a range of shapes. The shape ranges that are most successful (i.e., bind strongly to antigens) are given more exploration resources, in the form of mutated offspring lymphocytes, than the shape ranges that do not pan out (i.e., lymphocytes that do not bind strongly). However, while exploiting the information that has been obtained, the immune system continues at all times to generate new lymphocytes that explore completely novel shape ranges. Thus the system is able to focus on the most promising possibilities seen so far, while never neglecting to explore new possibilities.

Similarly, ant foraging uses a parallel-terraced-scan strategy: many ants initially explore random directions for food. If food is discovered in any of these directions, more of the system's resources (ants) are allocated, via the feedback mechanisms described above, to explore those directions further. At all times, different paths are dynamically allocated exploration resources in proportion to their relative promise (the amount and quality of the food that has been discovered at those locations). However, due to the large number of ants and their intrinsic random elements, unpromising paths continue to be explored as well, though with many fewer resources. After all, who knows-a better source of food might be discovered.

In cellular metabolism such fine-grained explorations are carried out by metabolic pathways, each focused on carrying out a particular task. A pathway can be speeded up or slowed down via feedback from its own results or from other pathways. The feedback itself is in the form of time-varying concentrations of molecules, so the relative speeds of different pathways can continually adapt to the moment-to-moment needs of the cell.

Note that the fine-grained nature of the system not only allows many different paths to be explored, but it also allows the system to continually change its exploration paths, since only relatively simple micro-actions are taken at any time. Employing more coa.r.s.e-grained actions would involve committing time to a particular exploration that might turn out not to be warranted. In this way, the fine-grained nature of exploration allows the system to fluidly and continuously adapt its exploration as a result of the information it obtains. Moreover, the redundancy inherent in fine-grained systems allows the system to work well even when the individual components are not perfectly reliable and the information available is only statistical in nature. Redundancy allows many independent samples of information to be made, and allows fine-grained actions to be consequential only when taken by large numbers of components.

Interplay of Unfocused and Focused Processes.

In all three example systems there is a continual interplay of unfocused, random explorations and focused actions driven by the system's perceived needs.

In the immune system, unfocused explorations are carried out by a continually changing population of lymphocytes with different receptors, collectively prepared to approximately match any antigen. Focused explorations consist of the creation of offspring that are variations of successful lymphocytes, which allow these explorations to zero in on a particular antigen shape.

Likewise, ant foraging consists of unfocused explorations by ants moving at random, looking for food in any direction, and focused explorations in which ants follow existing pheromone trails.

In cellular metabolism, unfocused processes of random exploration by molecules are combined with focused activation or inhibition driven by chemical concentrations and genetic regulation.

As in all adaptive systems, maintaining a correct balance between these two modes of exploring is essential. Indeed, the optimal balance shifts over time. Early explorations, based on little or no information, are largely random and unfocused. As information is obtained and acted on, exploration gradually becomes more deterministic and focused in response to what has been perceived by the system. In short, the system both explores to obtain information and exploits that information to successfully adapt. This balancing act between unfocused exploration and focused exploitation has been hypothesized to be a general property of adaptive and intelligent systems. John Holland, for example, has cited this balancing act as a way to explain how genetic algorithms work.

HOW DOES INFORMATION ACQUIRE MEANING?.

How information takes on meaning (some might call it purpose) is one of those slippery topics that has filled many a philosophy tome over the eons. I don't think I can add much to what the philosophers have said, but I do claim that in order to understand information processing in living systems we will need to answer this question in some form.

In my view, meaning is intimately tied up with survival and natural selection. Events that happen to an organism mean something to that organism if those events affect its well-being or reproductive abilities. In short, the meaning of an event is what tells one how to respond to it. Similarly, events that happen to or within an organism's immune system have meaning in terms of their effects on the fitness of the organism. (I'm using the term fitness informally here.) These events mean something to the immune system because they tell it how to respond so as to increase the organism's fitness-similarly with ant colonies, cells, and other information-processing systems in living creatures. This focus on fitness is one way I can make sense of the notion of meaning and apply it to biological information-processing systems.

But in a complex system such as those I've described above, in which simple components act without a central controller or leader, who or what actually perceives the meaning of situations so as to take appropriate actions? This is essentially the question of what const.i.tutes consciousness or self-awareness in living systems. To me this is among the most profound mysteries in complex systems and in science in general. Although this mystery has been the subject of many books of science and philosophy, it has not yet been completely explained to anyone's satisfaction.

Thinking about living systems as doing computation has had an interesting side effect: it has inspired computer scientists to write programs that mimic such systems in order to accomplish real-world tasks. For example, ideas about information processing in the immune system has inspired so-called artificial immune systems: programs that adaptively protect computers from viruses and other intruders. Similarly ant colonies have inspired what are now called "ant colony optimization algorithms," which use simulated ants, secreting simulated pheromones and switching between simulated jobs, to solve hard problems such as optimal cell-phone communications routing and optimal scheduling of delivery trucks. I don't know of any artificial intelligence programs inspired both by these two systems and by cellular metabolism, except for one I myself wrote with my Ph.D. advisor, which I describe in the next chapter.

CHAPTER 13.

How to Make a.n.a.logies (if You Are a Computer).

Easy Things Are Hard.

The other day I said to my eight-year-old son, "Jake, please put your socks on." He responded by putting them on his head. "See, I put my socks on!" He thought this was hilarious. I, on the other hand, realized that his antics ill.u.s.trated a deep truth about the difference between humans and computers.

The "socks on head" joke was funny (at least to an eight-year-old) because it violates something we all know is true: even though most statements in human language are, in principle, ambiguous, when you say something to another person, they almost always know what you mean. If I say to my husband, "Honey, do you know where my keys are?" and he replies, simply, "yes," I get annoyed-of course I meant "tell me where my keys are." When my best friend says that she is feeling swamped at her job, and I reply "same here," she knows that I don't mean that I am feeling swamped at her job, but rather my own. This mutual understanding is what we might call "common sense" or, more formally, "sensitivity to context."

In contrast, we have modern-day computers, which are anything but sensitive to context. My computer supposedly has a state-of-the-art spam filter, but sometimes it can't figure out that a message with a "word" such as is likely to be spam. As a similar example, a recent New York Times article described how print journalists are now learning how to improve the Web accessibility of their stories by tailoring headlines to literal-minded search engines instead of to savvy humans: "About a year ago, the Sacramento Bee changed online section t.i.tles. 'Real Estate' became 'Homes,' 'Scene' turned into 'Lifestyle,' and dining information found in newsprint under 'Taste,' is online under 'Taste/Food.' "