Arrival of the Fittest: Solving Evolution's Greatest Puzzle

by Andreas Wagner

  We have, in truth, learned nothing from the genome other than probabilities. How does a 1 or 3 percent increased risk for something translate into the clinic? It is useless information.50

  This assessment is stark, but it holds a grain of truth. You guessed the reason: The relationship between genotype and phenotype is complex beyond imagination. The Human Genome Project is only a mile marker on the journey from genotype to phenotype. It’s not anywhere close to the end of the road.

  Whatever its limitations, the genome project had many other benefits. One of them was that it whipped DNA sequencing technologies to blazing speeds. While in the year 2000 one person could read up to a million DNA letters in twenty-four hours, sequencing machines available in 2008 could already read up to a billion letters within the same span, and technologies have gotten much faster since then. As of this writing, full sequencing of a human genome costs little more than $1,000, and by the time you read this, the cost may have dropped to pennies. These technologies allow us to study genomic variation in large human populations and in many other organisms. They have transformed population genetics into population genomics.

  Population genomics is the end of the road for studying genotypes. The same cannot be said for the phenotype. The molecular biology work that began in the mid-1950s to unravel the functions of proteins and their interactions continues undiminished. But in the 1990s it had to take a new tack to progress further. For processes like insulin signaling, it had previously identified key genes, the proteins they encoded, what these proteins do, and which of them interact.51 All this information is like a who-is-who and a who-knows-whom of the cell. In the 1990s it became clear that such a catalog would fall short of predicting phenotypes, such as whether a person would develop diabetes. It fails to capture many subtleties that matter, the number of protein molecules involved, how firm their handshakes are, and so on. Dozens of different kinds of molecules contribute to diabetes, each contributing only a few percent to increased disease risk, but each conspiring with multiple others in subtle—and ill-understood—ways to cause disease. For these reasons it would not get us anywhere to merely list these molecules and their properties. We need to understand exactly how these molecular parts cooperate to form a whole phenotype.

  The only tools that could offer this integration are mathematical. Equations can encapsulate a wealth of experimental data and describe how the concentrations and activities of molecules change over time.52 And these activities are key to understanding phenotypes. For example, in type 2 diabetes the body shows insulin resistance, a phenotype different from that of a healthy individual: The pancreas releases insulin, but the liver reacts sluggishly. Somewhere along the signaling chain starting at the insulin receptor, the handshake of signaling molecules has become too weak (or too firm).53 And this change percolates down the signaling chain to cause disease and suffering. Only the rigorous quantification of mathematics can help us understand such subtleties. No mere catalog of molecules could achieve that.

  There is only one catch with the equations that can describe molecular phenotypes: They are not simple. They have many variables—molecules and their interactions—distilled from decades of experiments. They cannot be solved with pencil and paper. They are beyond the capabilities of even today’s most skilled mathematicians. Their solution requires computers.

  Computers have become as essential to twenty-first-century biology as digital cameras have become to photography. Computers do more than just run scientific equipment—from ultracold freezers to espresso machines—they are now instruments in their own right. Like the microscopes of the seventeenth century, they allow us to travel a new world, one so small that the most powerful imaging technologies, including electron microscopes, cannot resolve it: the world of molecules. Indeed, computers are the microscopes of the twenty-first century. They help us understand molecular webs that Darwin did not even know existed.

  This centrality of computing is new, because for much of its history, biology was limited by data. Early explorers had to voyage for years to discover new life forms in faraway lands. Even early in the molecular era isolating a single gene could be years of work. No longer. Thanks to ever-accelerating technology, thousands of ever-growing databases are overflowing with biological information, not just about genes and genomes, but also about the millions of molecular parts living things harbor, about what these parts do and which other parts they interact with. Every year now, gigabytes and terabytes of new data enter these databases. A new generation of scientists—computational biologists—uses only knowledge gathered by others, and no longer experiments with living organisms. Biologists are being transformed into information scientists, with access to nearly limitless data. The limits exist in our imagination, and in our skills to detect laws of nature in that data.

  These skills will surely be challenged, because the puzzle of how new phenotypes come into being has stymied science for more than a century. It’s one thing to recognize that phenotypes are like enormous pointillist paintings, created one molecular change at a time. It’s another to use that insight to understand how those paintings are actually created. The challenge is daunting, even on the smallest scale of proteins like the alcoholdehydrogenase that stands between you and Death by Happy Hour, since there are more ways to string amino acids together than there are hydrogen atoms in the entire universe. Referring to random change, recited like a mantra since Darwin’s time, as a source of all innovation is about as helpful as Anaximander’s argument that humans originated inside fish. It sweeps our ignorance under the rug by giving it a different name. This doesn’t mean that mutations don’t matter, or that natural selection isn’t absolutely necessary.54 But given the staggering odds, selection is not enough. We need a principle that accelerates innovation.

  Until a few years ago this principle was not merely unknown but beyond reach, and this book could not have been written. Because life is built of molecules, we need to understand molecules to understand innovation: not only the genotype embodied in DNA, but how this genotype helps build a phenotype. And a phenotype like that of a human body is not just a string of DNA. It is a hierarchy of being that descends from the visible organism, its tissues and cells, to the molecular webs formed by metabolic molecules, signaling molecules, and many others, extending down to the level of individual proteins. New phenotypes can originate at each level. A mere thirty years ago, we knew little of this staggering complexity.

  And if we knew little, just imagine how much less Darwin knew. The list of things that he didn’t know is practically an encyclopedia of modern biology. He wasn’t just ignorant of how phenotypes were inherited. He also had no knowledge, in those pre-Mendelian days, of genes, to say nothing of DNA and the genetic code. He also knew nothing of population genetics and little of developmental biology—he was oblivious to how molecules build bodies. He had no inkling of life’s true complexity (and many after him thought they could safely neglect it). But to crack the secret of innovation, we need to embrace it.

  The time-honored way to study life’s complexity is to focus on one or a few genotypes and their phenotype. This is how early geneticists found many genes in the first place—by tracking a phenotypic change back to its origin in a mutated gene. Later in the genome era, the same idea worked well to find out what a stretch of DNA does: Mutate it and see what happens to the phenotype. These strategies led to striking discoveries, mutations in genes that create flies with two pairs of wings instead of one, plants with transformed leaves, microbes able to survive on new foods. They created many examples of mutant genotypes and strangely altered phenotypes.

  The problem is that examples are not enough. Explorers cannot chart a newly discovered continent by making a single landfall and taking a walk on the beach. They need to circumnavigate it to draw its contours. They have to sail into its interior from its river deltas. And they need to traverse its mountain ranges, deserts, and jungles. We need to do just that to draw the elusive maps of life’s creativity�
��the genotype-phenotype maps that chart each change in a genotype and how it affects the phenotype. We need genotype-phenotype maps to complete Darwin’s job.55

  Even with the best technologies, these maps are not easy to draw. For a high-resolution map, we would need to understand the intricately folded phenotypes of more than 10130 different amino acid strings, and that’s before adding any of the higher layers of a phenotype, brought forth by thousands of genes and proteins. In other words, drawing a high-resolution map is not just hard but impossible. Luckily, though, we do not have to map every grain of sand in this new continent. If we care just about its topographical features, we can get away with studying fewer genotypes. But we still need to examine thousands to millions of them. And therefore we need to choose carefully which of the myriad aspects of phenotype we study. We need to choose those that are important for innovation in life’s history, and where existing information or predictive technology is sufficient to draw the map.

  In these maps, Platonic essentialism is making a comeback, after decades in which it served as the antihero of evolutionism.56 The essentialism of the twenty-first century, though, is much richer than Plato’s world of simple geometric shapes. It reveals a world full of meaning, compatible with Darwinism but going far beyond it, that is key to understanding how nature creates. This world is inaccessible to our naked eyes, just like the question of whether all four legs of a galloping Sallie Gardner leave the ground, but we can explore it with the best technologies currently available.

  These technologies have helped us reveal a Platonic world of crystalline splendor, the foundation of life’s innovability, which began with life’s very origin some four billion years ago.

  CHAPTER TWO

  The Origin of Innovation

  Here is an amazing experiment you can try at home. Put wheat in a container and seal the opening with dirty underwear. Wait twenty-one days, and mice will emerge. Not just newborn mice, but grown adult mice. At least that’s what the seventeenth-century physician and chemist Jan Baptista van Helmont reported.1 (He also revealed that scorpions would emerge from basil placed between two bricks and warmed by sunlight.)

  Van Helmont wasn’t the first to postulate the doctrine of spontaneous generation, which dates back at least to Aristotle, though he was among the last. Today, any scientist reporting that wheat and underwear conspire to create new life would be forever branded as a crackpot, but Van Helmont’s sloppy experiment did not cause much of a stir, and he died a respected man in 1644. Spontaneous generation was so widely accepted in his time that his experiments just proved the obvious.

  A few decades after Van Helmont’s death, the Italian physician Francesco Redi showed us how experiments like this should be done.2 Dump meat in a jar and in good time it will be crawling with maggots. But not spontaneously created ones: When Redi covered the jar with muslin, no maggots emerged, because flies could no longer deposit their eggs in the meat.

  Redi helped speed the decline of spontaneous creation. So did the seventeenth-century Dutch fabric merchant and lens grinder Antonie van Leeuwenhoek, whose microscopes opened the door to the world of microbes. For a time, microbes, being so much smaller than visible life, offered refuge to the remaining advocates of spontaneous generation. These were people like the Scottish priest John Needham, who argued in the mid-eighteenth century that decaying organic matter created microbes.3 Another century later Louis Pasteur would show that Needham had it backward: Microbes cause the decay of organic matter, not the other way around. Pasteur hammered the last nail into the coffin of spontaneous creation when he sterilized a nutrient broth and the air around it and showed that it remained lifeless.4

  Pasteur could show that spontaneous generation didn’t exist, but he and his contemporaries could not have known why: The origin of life is a problem for chemists, not biologists. And chemists in the nineteenth century suffered from the same disease as the Mendelists who tried to understand new variation in the early twentieth century: They were born too early. Dmitri Mendeleev had barely worked out the periodic table of the elements, and the chemistry of life was a big blank spot. Chemistry in general took a long time to become a respectable science in its own right, perhaps because of its deep roots in alchemy. Well into the twentieth century, after his first wife had run off with a chemist, the Nobel Prize–winning quantum physicist Wolfgang Pauli would remark to a friend that “had she taken a bullfighter I would have understood, but an ordinary chemist . . .”5

  A century later we know that the overwhelming obstacle facing spontaneous generation is probability, or rather improbability, resulting from life’s enormously complex phenotypes. If even a single protein, a single specific sequence of amino acids, could not have emerged spontaneously, how much less so could a bacterium like E. coli with millions of proteins and other complex molecules? Modern biochemistry allows us to estimate the odds, and they demolish the spontaneous creation of complex organisms.

  This does not mean that spontaneous creation did not occur in life’s early history. A natural origin of life even requires it, but in a much humbler form than a modern cell or even a modern protein. Earth’s first life form was far more like an oxcart than a Ferrari. In fact, it was a lot more like a wheel than an oxcart. And even this wheel was not created in one giant leap, but in many modest steps. Although the muck of deep time has eroded their footprints, chemists have reconstituted some of these steps, which are the subject of this chapter. They not only illustrate how it could have happened but prove an even more important point: Even before life itself arose, nature’s creativity used the same principles it uses today. Then and now, the new and improved arrives through new chemical reactions and molecules.

  The Hadean Eon, which marks the beginning of earth’s geological history more than four billion years ago, is aptly named after the Greek underworld, because the early earth was a hellish place. It began with a surface of liquid magma surrounded by an atmosphere of vaporized rock.6 And even after the surface had congealed into a solid crust, Mother Earth was not an inviting place. Had you visited the Hadean earth from outer space, you would have seen a tortured skin pockmarked with countless volcanoes, steamed by scalding rains that poured into the primordial oceans. Only the enormous pressure of the atmosphere—much denser than today—prevented these oceans from boiling away. Needless to say, breathing this atmosphere would have felled you instantaneously, noxious as it was from deadly amounts of carbon dioxide and hydrogen.7 Ducking for cover might also have been smart, for multiple giant asteroids tore into early earth during a period called the Late Heavy Bombardment. You can still shudder at their scars, giant craters visible nightly on the moon, even though the churning continents here on earth have erased most visible traces of these ancient cataclysms. We know their age—and that of earth itself—from ancient rocks that contain slowly ticking chemical clocks, materials like uranium, whose radioactive decay marks the passing eons.

  Most remarkable about this period is the speed with which life got going once the worst was over, just about 3.8 billion years ago. A mere few hundred million years later—less than 10 percent of earth’s age today—the first fossilized microbes appear.8 Even closer to the magic boundary of 3.8 billion years ago, telltale traces of an ancient metabolism in the form of light isotopes of carbon appear in rocks from West Greenland.9 Life wasted no time, and appeared almost as soon as it could appear. This tells us that life’s origin and the innovations behind it might not be that hard to come by. And that innovability is as old as life itself.

  Life’s early appearance on earth demands a theory of its chemical origin. Among the earliest ones is the “primordial soup” theory, usually credited to Alexander Oparin and J. B. S. Haldane, the Haldane of modern synthesis fame, who wrote about it in the 1920s.10 Remarkably, however, the ever-prescient Charles Darwin had this idea half a century before them. In an 1871 letter to his friend Joseph Dalton Hooker, he speculates, “If (and oh what a big if) we could conceive in some warm little pond with all sorts
of ammonia phosphoric salts, light, heat, electricity etc present, that a protein compound was chemically formed, ready to undergo still more complex changes.” And in the same breath Darwin gives us a good reason why we might look in vain for such a warm little pond today: Its content would be instantly “absorbed or devoured” by today’s organisms.11

  The primordial soup remained speculation for decades, until 1952, when it received a huge boost from Stanley Miller, a graduate student in the laboratory of Nobel laureate Harold Urey at the University of Chicago. Based on an informed guess at the composition of the gases that were present in the early atmosphere, Miller sealed these gases in a container, showered them with electric sparks to simulate primordial lightning, and washed the mixture in a rainfall of condensing water. After mere days, many organic molecules—those normally created by living organisms—had appeared in Miller’s miniature world. This was a monumental discovery, for it showed how organic molecules could have emerged from inorganic matter during the turbulent youth of our planet.12 And Miller’s primordial ocean produced not just any organic molecules. It created amino acids such as glycine and alanine, basic building blocks of modern proteins.13 Later experiments produced many other of life’s construction materials, including sugars and parts of DNA.14 But even more important was that Miller’s experiments moved life’s origin from philosophical speculation to the realm of hard, experimental science.

  In September 1969, the world learned something that Miller had not known in 1952: Life’s molecules can emerge in environments even more hostile than that of the early earth. That September an exploding fireball briefly created a second sun in the sky over Murchison, an Australian town of a few hundred souls some one hundred miles north of Melbourne. After fracturing, the meteorite left a trail of smoke and smaller fragments, the largest of which fell harmlessly into a barn. This cosmic accident happened two months after man had first walked on the moon, at a time when scientists were itching to study extraterrestrial rocks.