Brown and Enquist suspected that the answer lay somewhere in the structure of the systems in organisms that transport nutrients to cells. Blood constantly circulates in blood vessels, which form a branching network that carries nutrient chemicals to all cells in the body. Similarly, the branching structures in the lungs, called bronchi, carry oxygen from the lungs to the blood vessels that feed it into the blood (figure 17.3). Brown and Enquist believed that it is the universality of such branching structures in animals that give rise to the quarter-power laws. In order to understand how such structures might give rise to quarter-power laws, they needed to figure out how to describe these structures mathematically and to show that the math leads directly to the observed scaling laws.

Most biologists, Brown and Enquist included, do not have the math background necessary to construct such a complex geometric and topological a.n.a.lysis. So Brown and Enquist went in search of a "math buddy"-a mathematician or theoretical physicist who could help them out with this problem but not simplify it so much that the biology would get lost in the process.

Left to right: Geoffrey West, Brian Enquist, and James Brown. (Photograph copyright by Santa Fe Inst.i.tute. Reprinted with permission.) FIGURE 17.3. Ill.u.s.tration of bronchi, branching structures in the lungs. (Ill.u.s.tration by Patrick Lynch, licensed under Creative Commons [http://creativecommons.org/licenses/by/3.0/].) Enter Geoffrey West, who fit the bill perfectly. West, a theoretical physicist then working at Los Alamos National Laboratory, had the ideal mathematical skills to address the scaling problem. Not only had he already worked on the topic of scaling, albeit in the domain of quantum physics, but he himself had been mulling over the biological scaling problem as well, without knowing very much about biology. Brown and Enquist encountered West at the Santa Fe Inst.i.tute in the mid-1990s, and the three began to meet weekly at the inst.i.tute to forge a collaboration. I remember seeing them there once a week, in a gla.s.s-walled conference room, talking intently while someone (usually Geoffrey) was scrawling reams of complex equations on the white board. (Brian Enquist later described the group's math results as "pyrotechnics.") I knew only vaguely what they were up to. But later, when I first heard Geoffrey West give a lecture on their theory, I was awed by its elegance and scope. It seemed to me that this work was at the apex of what the field of complex systems had accomplished.

Brown, Enquist, and West had developed a theory that not only explained Kleiber's law and other observed biological scaling relationships but also predicted a number of new scaling relationships in living systems. Many of these have since been supported by data. The theory, called metabolic scaling theory (or simply metabolic theory), combines biology and physics in equal parts, and has ignited both fields with equal parts excitement and controversy.

Power Laws and Fractals.

Metabolic scaling theory answers two questions: (1) why metabolic scaling follows a power law at all; and (2) why it follows the particular power law with exponent 3/4. Before I describe how it answers these questions, I need to take a brief diversion to describe the relationship between power laws and fractals.

Remember the Koch curve and our discussion of fractals from chapter 7? If so, you might recall the notion of "fractal dimension." We saw that in the Koch curve, at each level the line segments were one-third the length of the previous level, and the structure at each level was made up of four copies of the structure at the previous level. In a.n.a.logy with the traditional definition of dimension, we defined the fractal dimension of the Koch curve this way: 3dimension = 4, which yields dimension = 1.26. More generally, if each level is scaled by a factor of x from the previous level and is made up of N copies of the previous level, then xdimension = N. Now, after having read chapter 15, you can recognize that this is a power law, with dimension as the exponent. This ill.u.s.trates the intimate relationship between power laws and fractals. Power law distributions, as we saw in chapter 15, figure 15.6, are fractals-they are self-similar at all scales of magnification, and a power-law's exponent gives the dimension of the corresponding fractal (cf. chapter 7), where the dimension quantifies precisely how the distribution's self-similarity scales with level of magnification. Thus one could say, for example, that the degree distributions of the Web has a fractal structure, since it is self-similar. Similarly one could say that a fractal like the Koch curve gives rise to a power-law-the one that describes precisely how the curve's self-similarity scales with level of magnification.

The take-home message is that fractal structure is one way to generate a power-law distribution; and if you happen to see that some quant.i.ty (such as metabolic rate) follows a power-law distribution, then you can hypothesize that there is something about the underlying system that is self-similar or "fractal-like."

Metabolic Scaling Theory.

Since metabolic rate is the rate at which the body's cells turn fuel into energy, Brown, Enquist, and West reasoned that metabolic rate must be largely determined by how efficiently that fuel is delivered to cells. It is the job of the organism's circulatory system to deliver this fuel.

Brown, Enquist, and West realized that the circulatory system is not just characterized in terms of its ma.s.s or length, but rather in terms of its network structure. As West pointed out, "You really have to think in terms of two separate scales-the length of the superficial you and the real you, which is made up of networks."

In developing their theory, Brown, Enquist, and West a.s.sumed that evolution has produced circulatory and other fuel-transport networks that are maximally "s.p.a.ce filling" in the body-that is, that can transport fuel to cells in every part of the body. They also a.s.sumed that evolution has designed these networks to minimize the energy and time that is required to distribute this fuel to cells. Finally, they a.s.sume that the "terminal units" of the network, the sites where fuel is provided to body tissue, do not scale with body ma.s.s, but rather are approximately the same size in small and large organisms. This property has been observed, for example, with capillaries in the circulatory system, which are the same size in most animals. Big animals just have more of them. One reason for this is that cells themselves do not scale with body size: individual mouse and hippo cells are roughly the same size. The hippo just has more cells so needs more capillaries to fuel them.

The maximally s.p.a.ce-filling geometric objects are indeed fractal branching structures-the self-similarity at all scales means that s.p.a.ce is equally filled at all scales. What Brown, Enquist, and West were doing in the gla.s.s-walled conference room all those many weeks and months was developing an intricate mathematical model of the circulatory system as a s.p.a.ce-filling fractal. They adopted the energy-and-time-minimization and constant-terminal-unit-size a.s.sumptions given above, and asked, What happens in the model when body ma.s.s is scaled up? Lo and behold, their calculations showed that in the model, the rate at which fuel is delivered to cells, which determines metabolic rate, scales with body ma.s.s to the 3/4 power.

The mathematical details of the model that lead to the 3/4 exponent are rather complicated. However, it is worth commenting on the group's interpretation of the 3/4 exponent. Recall my discussion above of Rubner's surface hypothesis-that metabolic rate must scale with body ma.s.s the same way in which volume scales with surface area, namely, to the 2/3 power. One way to look at the 3/4 exponent is that it would be the result of the surface hypothesis applied to four-dimensional creatures! We can see this via a simple dimensional a.n.a.logy. A two-dimensional object such as a circle has a circ.u.mference and an area. In three dimensions, these correspond to surface area and volume, respectively. In four dimensions, surface area and volume correspond, respectively, to "surface" volume and what we might call hypervolume-a quant.i.ty that is hard to imagine since our brains are wired to think in three, not four dimensions. Using arguments that are a.n.a.logous to the discussion of how surface area scales with volume to the 2/3 power, one can show that in four dimensions surface volume scales with hypervolume to the 3/4 power.

In short, what Brown, Enquist, and West are saying is that evolution structured our circulatory systems as fractal networks to approximate a "fourth dimension" so as to make our metabolisms more efficient. As West, Brown, and Enquist put it, "Although living things occupy a three-dimensional s.p.a.ce, their internal physiology and anatomy operate as if they were four-dimensional ... Fractal geometry has literally given life an added dimension."

Scope of the Theory.

In its original form, metabolic scaling theory was applied to explain metabolic scaling in many animal species, such as those plotted in figure 17.2. However, Brown, Enquist, West, and their increasing cadre of new collaborators did not stop there. Every few weeks, it seems, a new cla.s.s of organisms or phenomena is added to the list covered by the theory. The group has claimed that their theory can also be used to explain other quarter-power scaling laws such as those governing heart rate, life span, gestation time, and time spent sleeping.

The group also believes that the theory explains metabolic scaling in plants, many of which use fractal-like vascular networks to transport water and other nutrients. They further claim that the theory explains the quarter-power scaling laws for tree trunk circ.u.mference, plant growth rates, and several other aspects of animal and plant organisms alike. A more general form of the metabolic scaling theory that includes body temperature was proposed to explain metabolic rates in reptiles and fish.

Moving to the microscopic realm, the group has postulated that their theory applies at the cellular level, a.s.serting that 3/4 power metabolic scaling predicts the metabolic rate of single-celled organisms as well as of metabolic-like, molecule-sized distribution processes inside the cell itself, and even to metabolic-like processes inside components of cells such as mitochondria. The group also proposed that the theory explains the rate of DNA changes in organisms, and thus is highly relevant to both genetics and evolutionary biology. Others have reported that the theory explains the scaling of ma.s.s versus growth rate in cancerous tumors.

In the realm of the very large, metabolic scaling theory and its extensions have been applied to entire ecosystems. Brown, Enquist, and West believe that their theory explains the observed 3/4 scaling of species population density with body size in certain ecosystems.

In fact, because metabolism is so central to all aspects of life, it's hard to find an area of biology that this theory doesn't touch on. As you can imagine, this has got many scientists very excited and looking for new places to apply the theory. Metabolic scaling theory has been said to have "the potential to unify all of biology" and to be "as potentially important to biology as Newton's contributions are to physics." In one of their papers, the group themselves commented, "We see the prospects for the emergence of a general theory of metabolism that will play a role in biology similar to the theory of genetics."

Controversy.

As to be expected for a relatively new, high-profile theory that claims to explain so much, while some scientists are bursting with enthusiasm for metabolic scaling theory, others are roiling with criticism. Here are the two main criticisms that are currently being published in some of the top scientific journals: Quarter-power scaling laws are not as universal as the theory claims. As a rule, given any proposed general property of living systems, biology exhibits exceptions to the rule. (And maybe even exceptions to this rule itself.) Metabolic scaling theory is no exception, so to speak. Although most biologists agree that a large number of species seem to follow the various quarter-power scaling laws, there are also many exceptions, and sometimes there is considerable variation in metabolic rate even within a single species. One familiar example is dogs, in which smaller breeds tend to live at least as long as larger breeds. It has been argued that, while Kleiber's law represents a statistical average, the variations from this average can be quite large, and metabolic theory does not explain this because it takes into account only body ma.s.s and temperature. Others have argued that there are laws predicted by the theory that real-world data strongly contradict. Still others argue that Kleiber was wrong all along, and the best fit to the data is actually a power law with exponent 2/3, as proposed over one hundred years ago by Rubner in his surface hypothesis. In most cases, this is an argument about how to correctly interpret data on metabolic scaling and about what const.i.tutes a "fit" to the theory. The metabolic scaling group stands by its theory, and has diligently replied to many of these arguments, which become increasingly technical and obscure as the authors discuss the intricacies of advanced statistics and biological functions.

The Kleiber scaling law is valid but the metabolic scaling theory is wrong. Others have argued that metabolic scaling theory is oversimplified, that life is too complex and varied to be covered by one overreaching theory, and that positing fractal structure is by no means the only way to explain the observed power-law distributions. One ecologist put it this way: "The more detail that one knows about the particular physiology involved, the less plausible these explanations become." Another warned, "It's nice when things are simple, but the real world isn't always so." Finally, there have been arguments that the mathematics in metabolic scaling theory is incorrect. The authors of metabolic scaling theory have vehemently disagreed with these critiques and in some cases have pointed out what they believed to be fundamental mistakes in the critic's mathematics.

The authors of metabolic scaling theory have strongly stood by their work and expressed frustration about criticisms of details. As West said, "Part of me doesn't want to be cowered by these little dogs nipping at our heels." However, the group also recognizes that a deluge of such criticisms is a good sign-whatever they end up believing, a very large number of people have sat up and taken notice of metabolic scaling theory. And of course, as I have mentioned, skepticism is one of the most important jobs of scientists, and the more prominent the theory and the more ambitious its claims are, the more skepticism is warranted.

The arguments will not end soon; after all, Newton's theory of gravity was not widely accepted for more than sixty years after it first appeared, and many other of the most important scientific advances have faced similar fates. The main conclusion we can reach is that metabolic scaling theory is an exceptionally interesting idea with a huge scope and some experimental support. As ecologist Helene Muller-Landau predicts: "I suspect that West, Enquist et al. will continue repeating their central arguments and others will continue repeating the same central critiques, for years to come, until the weight of evidence finally leads one way or the other to win out."

The Unresolved Mystery of Power Laws.

We have seen a lot of power laws in this and the previous chapters. In addition to these, power-law distributions have been identified for the size of cities, people's incomes, earthquakes, variability in heart rate, forest fires, and stock-market volatility, to name just a few phenomena.

As I described in chapter 15, scientists typically a.s.sume that most natural phenomena are distributed according to the bell curve or normal distribution. However, power laws are being discovered in such a great number and variety of phenomena that some scientists are calling them "more normal than 'normal.'" In the words of mathematician Walter Willinger and his colleagues: "The presence of [power-law] distributions in data obtained from complex natural or engineered systems should be considered the norm rather than the exception."

Scientists have a pretty good handle on what gives rise to bell curve distributions in nature, but power laws are something of a mystery. As we have seen, there are many different explanations for the power laws observed in nature (e.g., preferential attachment, fractal structure, self-organized criticality, highly optimized tolerance, among others), and little agreement on which observed power laws are caused by which mechanisms.

In the early 1930s, a Harvard professor of linguistics, George Kingsley Zipf, published a book in which he included an interesting property of language. First take any large text such as a novel or a newspaper, and list each word in the order of how many times it appears. For example, here is a partial list of words and frequencies from Shakespeare's "To be or not to be" monologue from the play Hamlet: Putting this list in order of decreasing frequencies, we can a.s.sign a rank of 1 to the most frequent word (here, "the"), a rank of 2 to the second most frequent word, and so on. Some words are tied for frequency (e.g., "a" and "sleep" both have five occurrences). Here, I have broken ties for ranking at random.

In figure 17.4, I have plotted the to-be-or-not-to-be word frequency as a function of rank. The shape of the plot indeed looks like a power law. If the text I had chosen had been larger, the graph would have looked even more power-law-ish.

Zipf a.n.a.lyzed large amounts of text in this way (without the help of computers!) and found that, given a large text, the frequency of a word is approximately proportional to the inverse of its rank (i.e., 1 /rank). This is a power law, with exponent 1. The second highest ranked word will appear about half as often as the first, the third about one-third as often, and so forth. This relation is now called Zipf's law, and is perhaps the most famous of known power laws.

FIGURE 17.4. An ill.u.s.tration of Zipf's law using Shakespeare's "To be or not to be" monologue.

There have been many different explanations proposed for Zipf's law. Zipf himself proposed that, on the one hand, people in general operate by a "Principle of Least Effort": once a word has been used, it takes less effort to use it again for similar meanings than to come up with a different word. On the other hand, people want language to be unambiguous, which they can accomplish by using different words for similar but nonidentical meanings. Zipf showed mathematically that these two pressures working together could produce the observed power-law distribution.

In the 1950s, Benoit Mandelbrot, of fractal fame, had a somewhat different explanation, in terms of information content. Following Claude Shannon's formulation of information theory (cf. chapter 3), Mandelbrot considered a word as a "message" being sent from a "source" who wants to maximize the amount of information while minimizing the cost of sending that information. For example, the words feline and cat mean the same thing, but the latter, being shorter, costs less (or takes less energy) to transmit. Mandelbrot showed that if the information content and transmission costs are simultaneously optimized, the result is Zipf's law.

At about the same time, Herbert Simon proposed yet another explanation, presaging the notion of preferential attachment. Simon envisioned a person adding words one at a time to a text. He proposed that at any time, the probability of that person reusing a word is proportional to that word's current frequency in the text. All words that have not yet appeared have the same, nonzero probability of being added. Simon showed that this process results in text that follows Zipf's law.

Evidently Mandelbrot and Simon had a rather heated argument (via dueling letters to the journal Information and Control) about whose explanation was correct.

Finally, also around the same time, to everyone's amus.e.m.e.nt or chagrin, the psychologist George Miller showed, using simple probability theory, that the text generated by monkeys typing randomly on a keyboard, ending a word every time they (randomly) hit the s.p.a.ce bar, will follow Zipf's law as well.

The many explanations of Zipf's law proposed in the 1930s through the 1950s epitomize the arguments going on at present concerning the physical or informational mechanisms giving rise to power laws in nature. Understanding power-law distributions, their origins, their significance, and their commonalities across disciplines is currently a very important open problem in many areas of complex systems research. It is an issue I'm sure you will hear more about as the science behind these laws becomes clearer.

CHAPTER 18.

Evolution, Complexified.

IN CHAPTER I I asked, "How did evolution produce creatures with such an enormous contrast between their individual simplicity and their collective sophistication?" Indeed, as ill.u.s.trated by the examples we've seen in this book, the closer one looks at living systems, the more astonishing it seems that such intricate complexity could have been formed by the gradual acc.u.mulation of favorable mutations or the whims of historical accident. This very argument has been used from Darwin's time to the present by believers in divine creation or other supernatural means of "intelligent design."

The questions of how, why, and even if evolution creates complexity, and how complexity in biology might be characterized and measured, are still very much open. One of the most important contributions of complex systems research over the last few decades has been to demonstrate new ways to approach these age-old questions. In this chapter I describe some of the recent discoveries in genetics and the dynamics of genetic regulation that are giving us surprising new insights into the evolution of complex systems.

Genetics, Complexified.

Often in science new technologies can open a floodgate of discoveries that change scientists' views of a previously established field of study. We saw an example of this back in chapter 2-it was the invention of the electronic computer, and its capacity for modeling complex systems such as weather, that allowed for the demonstration of the existence of chaos. More recently, extremely powerful land and s.p.a.ce-based telescopes have led to a flurry of discoveries in astronomy concerning so-called dark matter and dark energy, which seem to call into question much of what was previously accepted in cosmology.

No new set of technologies has had a more profound impact on an established field than the so-called molecular revolution in genetics over the last four decades. Technologies for rapidly copying, sequencing, synthesizing, and engineering DNA, for imaging molecular-level structures that had never been seen before, and for viewing expression patterns of thousands of different genes simultaneously; these are only a few examples of the feats of biotechnology in the late twentieth and early twenty-first centuries. And it seems that with each new technology allowing biologists to peer closer into the cell, more unexpected complexities appear.

At the time Watson and Crick discovered its structure, DNA was basically thought of as a string of genes, each of which coded for a particular protein that carried out some function in the cell. This string of genes was viewed essentially as the "computer program" of the cell, whose commands were translated and enacted by RNA, ribosomes, and the like, in order to synthesize the proteins that the genes stood for. Small random changes to the genome occurred when copying errors were made during the DNA duplication process; the long-term acc.u.mulation of those small random changes that happened to be favorable were the ultimate cause of adaptive change in biology and the origin of new species.

This conventional view has undergone monumental changes in the last 40 years. The term molecular revolution refers not only to the revolutionary new techniques in genetics, but also to the revolutionary new view of DNA, genes, and the nature of evolution that these techniques have provided.

What Is a Gene?

One casualty of the molecular revolution is the straightforward concept of gene. The mechanics of DNA that I sketched in chapter 6 still holds true-chromosomes contain stretches of DNA that are transcribed and translated to create proteins-but it turns out to be only part of the story. The following are a few examples that give the flavor of the many phenomena that have been and are being discovered; these phenomena are confounding the straightforward view of how genes and inheritance work.

Genes are not like "beads on a string." When I took high-school biology, genes and chromosomes were explained using the beads-on-a-string metaphor (and I think we even got to put together a model using pop-together plastic beads). However, it turns out that genes are not so discretely separated from one another. There are genes that overlap with other genes-i.e., they each code for a different protein, but they share DNA nucleotides. There are genes that are wholly contained inside other genes.

Genes move around on their chromosome and between chromosomes. You may have heard of "jumping genes." Indeed, genes can move around, rearranging the makeup of chromosomes. This can happen in any cell, including sperm and egg cells, meaning that the effects can be inherited. The result can be a much higher rate of mutation than comes from errors in DNA replication. Some scientists have proposed that these "mobile genetic elements" might be responsible for the differences observed between close relatives, and even between identical twins. The phenomenon of jumping genes has even been proposed as one of the mechanisms responsible for the diversity of life.

A single gene can code for more than one protein. It had long been thought that there was a one-to-one correspondence between genes and proteins. A problem for this a.s.sumption arose when the human genome was sequenced, and it was discovered that while the number of different types of proteins encoded by genes may exceed 100,000, the human genome contains only about 25,000 genes. The recently discovered phenomena of alternative splicing and RNA editing help explain this discrepancy. These processes can alter messenger RNA in various ways after it has transcribed DNA but before it is translated into amino acids. This means that different transcription events of the same gene can produce different final proteins.

In light of all these complications, even professional biologists don't always agree on the definition of "gene." Recently a group of science philosophers and biologists performed a survey in which 500 biologists were independently given certain unusual but real DNA sequences and asked whether each sequence qualified as a "gene," and how confident they were of their answer. It turned out that for many of the sequences, opinion was split, with about 60% confident of one answer and 40% confident of the other answer. As stated in an article in Nature reporting on this work, "The more expert scientists become in molecular genetics, the less easy it is to be sure about what, if anything, a gene actually is."

The complexity of living systems is largely due to networks of genes rather than the sum of independent effects of individual genes. As I described in chapter 16, genetic regulatory networks are currently a major focus of the field of genetics. In the old genes-as-beads-on-a-string view, as in Mendel's laws, genes are linear-each gene independently contributes to the entire phenotype. The new, generally accepted view, is that genes in a cell operate in nonlinear information-processing networks, in which some genes control the actions of other genes in response to changes in the cell's state-that is, genes do not operate independently.

There are heritable changes in the function of genes that can occur without any modification of the gene's DNA sequence. Such changes are studied in the growing field of epigenetics. One example is so-called DNA methylation, in which an enzyme in a cell attaches particular molecules to some parts of a DNA sequence, effectively "turning off" those parts. When this occurs in a cell, all descendents of that cell will have the same DNA methylation. Thus if DNA methylation occurs in a sperm or egg cell, it will be inherited.

On the one hand, this kind of epigenetic effect happens all the time in our cells, and is essential for life in many respects, turning off genes that are no longer needed (e.g., once we reach adulthood, we no longer need to grow and develop like a child; thus genes controlling juvenile development are methylated). On the other hand, incorrect or absent methylation is the cause of some genetic disorders and diseases. In fact, the absence of necessary methylation during embryo development is thought by some to be the reason so many cloned embryos do not survive to birth, or why so many cloned animals that do survive have serious, often fatal disorders.

It has recently been discovered that in most organisms a large proportion of the DNA that is transcribed by RNA is not subsequently translated into proteins. This so-called noncoding RNA can have many regulatory effects on genes, as well as functional roles in cells, both of which jobs were previously thought to be the sole purview of proteins. The significance of non-coding RNAs is currently a very active research topic in genetics.

Genetics has become very complicated indeed. And the implications of all these complications for biology are enormous. In 2003 the Human Genome Project published the entire human genome-that is, the complete sequence of human DNA. Although a tremendous amount was learned from this project, it was less than some had hoped. Some had believed that a complete mapping of human genes would provide a nearly complete understanding of how genetics worked, which genes were responsible for which traits, and that this would guide the way for revolutionary medical discoveries and targeted gene therapies. Although there have been several discoveries of certain genes that are implicated in particular diseases, it has turned out that simply knowing the sequence of DNA is not nearly enough to understand a person's (or any complex organism's) unique collection of traits and defects.

One sector that pinned high hopes on the sequencing of genes is the international biotechnology industry. A recent New York Times article reported on the effects that all this newly discovered genetic complexity was having on biotech: "The presumption that genes operate independently has been inst.i.tutionalized since 1976, when the first biotech company was founded. In fact, it is the economic and regulatory foundation on which the entire biotechnology industry is built."

The problem is not just that the science underlying genetics is being rapidly revised. A major issue lurking for biotech is the status of gene patents. For decades biotech companies have been patenting particular sequences of human DNA that were believed to "encode a specific functional product." But as we have seen above, many, if not most, complex traits are not determined by the exact DNA sequence of a particular gene. So are these patents defensible? What if the "functional product" is the result of epigenetic processes acting on the gene or its regulators? Or what if the product requires not only the patented gene but also the genes that regulate it, and the genes that regulate those genes, and so on? And what if those regulatory genes are patented by someone else? Once we leave the world of linear genes and encounter essential nonlinearity, the meaning of these patents becomes very murky and may guarantee the employment of patent lawyers and judges for a long time to come. And patents aren't the only problem. As the New York Times pointed out, "Evidence of a networked genome shatters the scientific basis for virtually every official risk a.s.sessment of today's commercial biotech products, from genetically engineered crops to pharmaceuticals."

Not only genetics, but evolutionary theory as a whole has been profoundly challenged by these new genetic discoveries. A prominent example of this is the field of "Evo-Devo."

Evo-Devo.

Evo-Devo is the nickname for "evolutionary developmental biology." Many people are very excited about this field and its recent discoveries, which are claimed to explain at least three big mysteries of genetics and evolution: (1) Humans have only about 25,000 genes. What is responsible for our complexity? (2) Genetically, humans are very similar to many other species. For example, more than 90% of our DNA is shared with mice and more than 95% with chimps. Why are our bodies so different from those of other animals? (3) Supposing that Stephen Jay Gould and others are correct about punctuated equilibria in evolution, how could big changes in body morphology happen in short periods of evolutionary time?

It has recently been proposed that the answer to these questions lies, at least in part, in the discovery of genetic switches.

The fields of developmental biology and embryology study the processes by which a fertilized single egg cell becomes a viable multibillion-celled living organism. However, the Modern Synthesis's concern was with genes; in the words of developmental biologist Sean Carroll, it treated developmental biology and embryology as a " 'black box' that somehow transformed genetic information into three-dimensional, functional animals." This was in part due to the view that the huge diversity of animal morphology would eventually be explained by large differences in the number of and DNA makeup of genes.

In the 1980s and 1990s, this view became widely challenged. As I noted above, DNA sequencing had revealed the extensive similarities in DNA among many different species. Advances in genetics also produced a detailed understanding of the mechanisms of gene expression in cells during embryonic and fetal development. These mechanisms turned out to be quite different from what was generally expected. Embryologists discovered that, in all complex animals under study, there is a small set of "master genes" that regulate the formation and morphology of many of the animal's body parts. Even more surprising, these master genes were found to share many of the same sequences of DNA across many species with extreme morphological differences, ranging from fruit flies to humans.

Given that their developmental processes are governed by the same genes, how is it that these different animals develop such different body parts? Proponents of Evo-Devo propose that morphological diversity among species is, for the most part, not due to differences in genes but in genetic switches that are used to turn genes on and off. These switches are sequences of DNA-often several hundred base pairs in length-that do not code for any protein. Rather they are part of what used to be called "junk DNA," but now have been found to be used in gene regulation.

Figure 18.1 ill.u.s.trates how switches work. A switch is a sequence of non-coding DNA that resides nearby a particular gene. This sequence of molecules typically contains on the order of a dozen signature subsequences, each of which chemically binds with a particular protein, that is, the protein attaches to the DNA string. Whether or not the nearby gene gets transcribed, and how quickly, depends on the combination of proteins attached to these subsequences. Proteins that allow transcription create strong binding sites for RNA molecules that will do the transcribing; proteins that prevent transcription block these same RNA molecules from binding to the DNA. Some of these proteins can negate the effects of others.