Arrival of the Fittest: Solving Evolution's Greatest Puzzle (31 page)

When Karthik analyzed logic circuits that differed in their complexity—their number of logic gates—he found that the simplest circuits could not be rewired without destroying their function. Change one wire in such a circuit, and you destroy the circuit’s function. Every gate and every wire matters. Such simple circuits have no innovability, because they cannot explore new configurations and computations. For rewiring, one needs more complex circuits. The more complex they are, the more rewiring they tolerate. Their apparently superfluous gates and wires are like collections of spare parts—piles of Edison’s precious junk—that help compute new digital functions. Just as in biology, innovability comes from complexity, apparently unnecessary, but actually vital. This is one of nature’s lessons for innovable technologies: If we want to open nature’s black box of innovation, Ockham’s razor is much too dull. Like oil and water, simplicity and innovability don’t mix.

This doesn’t mean that simplicity and elegance are absent from powerful innovable technologies. Quite the opposite. But they hide beneath the visible world. The basic principle behind them is simplicity itself: With a limited number of building blocks connected in a limited number of ways, you can create an entire world. Out of such building blocks and standard links between them, nature has created a world of proteins, regulation circuits, and metabolisms that sustains life, that has brought forth simple viruses and complex humans, and ultimately, our culture and technology, from the
Iliad
to the iPad. The simplicity and the elegance of innovable technologies are hidden behind the visible world, just like nature’s libraries, whose faint reflection we see in the Tree of Life, like a shadow in Plato’s cave.

I
n October 1970, the magazine
Scientific American
published a description of the Game of Life, a creation of the British mathematician John Conway, and a simplification of ideas on building self-replicating machines proposed by the polymath John von Neumann. Not requiring a human player, the “game” can unfold inside a computer on a two-dimensional grid of cells, each of which can be either “on” (alive) or “off” (dead). Each square on Conway’s grid has eight neighbors, and a very simple set of rules determines their status. For example, if a cell has fewer than two live neighbors it turns off. In the game’s lingo it “dies.” Same result if it has between four and eight live neighbors. However, if the cell has two or three “live” neighbors, it gets to live. The final rule: A dead cell with three live neighbors is reanimated.

And that’s it. But depending on which cells are on and off when the game starts, what follows is anything but simple. Enormously complex patterns can emerge, a huge and unpredictable variety of forms, including “self-replicating” clusters of cells that spawn more of themselves. And from these simple beginnings, the Game of Life can go on indefinitely, creating complex patterns that never repeat or terminate.

Like life itself.

The game is a metaphor rather than a model for life, but it reflects a broader human aspiration: to understand life and its diversity through the language of mathematics and computation. This aspiration is much older than the game. Seventeen years after the publication of the
Origin,
Charles Darwin wrote in his autobiography, “I have deeply regretted that I did not proceed far enough at least to understand something of the great leading principles of mathematics, for men thus endowed seem to have an extra sense.”
¹

Like the zoopraxiscope that filmed Sallie Gardner four years before Darwin’s death, Darwin’s work sparked a revolution, but even if he had been a mathematician he would have been in the dark about the hidden architecture of life—he could not even know that it existed. To illuminate nature’s giant libraries, the flames of this revolution would require more fuel than Darwin’s theory.

Biology and mathematics first needed to become fully intertwined, which would take another century. It began with the mathematics of Sewall Wright and R. A. Fisher, which bridged the gap between traditional Darwinism and Mendelian genetics, and led to the modern synthesis that allowed the first accurate predictions of how fast natural selection can help innovations spread. Another half century had to elapse until systems biology taught us how molecules cooperate to produce the complex behavior and phenotypes of life. In doing so, it showed us that cells are vastly more complex than the simple elements of the Game of Life. Through regulatory circuits operating a bit like the neural networks of our brains, they perform sophisticated computations that regulate their own molecules and help them survive. And while these circuits are very different from digital computers—for one thing, they are self-assembled from organic molecules—they hint at a deep unity between the material world of biology and the conceptual world of mathematics and computation, a unity that Conway and Darwin could barely have guessed at.

The mathematical perspective of systems biology also allowed us to decipher the staggeringly complex phenotypic meaning of genotypic texts in nature’s libraries, which is crucial to understanding innovability. It led us to identify genotype networks, and to grasp that genotype networks are the common origin of the different
kinds
of innovations—in metabolism, regulation, and macromolecules—that created life as we know it. They propelled life from its very beginnings to single-celled organisms, from our bacterial and eukaryotic ancestors to primitive wormlike creatures, fish, amphibians, mammals, and all the way to humans, spanning billions of generations.

More than that, the mathematics of biology allowed us to see that these libraries self-organize with a simple principle, as simple as the gravitation that helps mold diffuse matter into enormous galaxies. This principle—that organisms are robust, a consequence of the complexity that helps them survive in a changing world—brings forth the intricate organization of these vast libraries.

These libraries and their texts differ fundamentally from the muscles, nerves, and connective tissues that an anatomist dissects and that we can touch with our bare hands. They are not even like cellular organelles visible through a microscope, or the structure of DNA revealed by X-ray crystallography. They are concepts, mathematical concepts, touchable only by the mind’s eye.

Does that mean they exist only in our imagination? Did we discover them or invent them?

The question whether knowledge—especially mathematical knowledge—is created or discovered has occupied philosophers for more than twenty-five hundred years, at least since Pythagoras and certainly since Plato. Plato saw our visible world as a faint shadow cast by the light of a higher, timeless reality on the poorly lit walls of a cave we inhabit. Platonists posit that we
discover
truths, which come to us from a higher reality. They exist even if nobody is there to see them, like the dark side of the moon. Others, such as the Austrian philosopher Ludwig Wittgenstein, argue that mathematical truths are
invented
—in Wittgenstein’s words, “the mathematician is an inventor, not a discoverer.”
²

Platonism has the upper hand in this debate, even though Plato himself was unaware of the best argument for it. It is the startling congruence between mathematical theorems and physical reality, encapsulated in a dictum often attributed to Galileo Galilei: “Mathematics is the language in which God wrote the universe.” (Words that should give any naïve creationist pause.) The
Hungarian-born Nobel Prize–winning physicist and mathematician Eugene Wigner called it “the unreasonable effectiveness of mathematics in the natural sciences.”
³

Unreasonable indeed: We know no reason why Newton’s laws should predict so much more than the speed of a falling apple, phenomena as different as the rotation of planets and the shaping of galaxies. Except they do. And so do countless other mathematical laws that explain phenomena so remote in space and time that we will never experience them directly. The nexus between math and reality is so tight, in fact, that the Swedish theoretical physicist Max Tegmark argues that the entire universe
is
mathematics.
⁴

But the “unreasonable effectiveness” of math is not the only reason to believe in the reality of nature’s libraries and their genotype networks. Another is that the technology of the twenty-first century grants us unrestricted access to these libraries. In so doing it can shift the debate about discovery versus invention—uncomfortably abstract for millennia—from its traditional focus on languages like that of mathematics to incorporate experimental science. The reason is that we now can read individual volumes in nature’s libraries. We can, for example, manufacture any volume of the protein library—
any
amino acid sequence at all—and study its chemical meaning with the instruments of biochemistry. Many of these volumes were discovered by other organisms long before us, and their molecular meaning has surprised us greatly, as antifreeze proteins, crystallins, and Hox regulators testify. It’s a safe bet that nature’s libraries will continue to surprise us—more than anything we just invented.

When we begin to study nature’s libraries we aren’t just investigating life’s innovability or that of technology. We are shedding new light on one of the most durable and fascinating subjects in all of philosophy. And we learn that life’s creativity draws from a source that is older than life, and perhaps older than time.

Some key collaborators are mentioned by name in the text, but I am greatly indebted to numerous others, from graduate students to postdoctoral fellows and faculty colleagues at multiple universities. I am especially grateful for discussions with research associates in my laboratory, among them Aditya Barve, Sinisa Bratulic, Joshua Payne, José Aguilar-Rodríguez, and Kathleen Sprouffske. In many conversations superficially unrelated to this book, they have unknowingly sharpened my thinking about the material represented herein. My thanks also go to the numerous colleagues and fellow visitors whom I have encountered over the years at the Santa Fe Institute, which has remained a wellspring of new ideas and stimulation. Special thanks go to Jerry Sabloff and Doug Erwin, who provided feedback on an early draft of the manuscript. I am also indebted to Cormac McCarthy, who not only read this early draft but also provided many useful editorial comments. (Bowing to his avowed aversion to punctuation, this book is free of semicolons.) My faculty colleagues at the University of Zürich deserve thanks for helping create the kind of research environment in which projects like this can thrive.

Bill Rosen taught me that a good editor can turn caterpillars into butterflies. His guidance was instrumental at all stages of this project. He did an outstanding job and I cannot thank him enough. He and my agent, Lisa Adams, also helped me navigate the treacherous waters of the publishing industry. Lisa superbly handled all contractual matters. Furthermore, I am indebted to Niki Papadopoulos of Current for editorial support. Her incisive comments and questions have helped improve the manuscript greatly. She and her assistants, Kary Perez and Natalie Horbachevsky, have also promptly and patiently handled numerous queries. Last but not least, thanks go to my family for their benevolent tolerance of my moods when the roller coaster of the writing process went on one of its downturns.

PROLOGUE: WORLD ENOUGH, AND TIME

1
. The assumption that these processes proceeded through most of the earth’s history at the same rate as today is a principle of geology known as uniformitarianism.

2
. See Zimmer (2001), 60.

3
. See Burchfield (1974). A relevant passage can be found in chapter 10, page 338, of the sixth edition of Darwin’s central opus,
On the Origin of Species by Means of Natural Selection.
See Darwin (1872). Darwin published six English-language editions of this book in his lifetime, each different from the one before. In these endnotes I cite page numbers from the sixth edition, as reprinted by A. L. Burt (New York), but usually the first edition, i.e., Darwin (1859), is cited.

4
. See Burchfield (1990), 164. This often-quoted anecdote obscures the true reason for Kelvin’s error (which is unimportant for my point here). It was his assumption that thermal conductivity is uniform throughout the earth’s interior, as discussed in England, Molnar, and Richter (2007).

5
. See Sibley (2001).

6
. See Schwab (2012), 188, as well as Tucker (2000).

7. See Goldsmith (2006).

8
. Human nails and birds’ claws are composed of proteins from different keratin subfamilies, known as α-keratins and β-keratins respectively. See Greenwold and Sawyer (2011) for the origin of β-keratins.

9
. See Kappe et al. (2010).

10
. See Shimeld et al. (2005) and Feuda et al. (2012). Vertebrates themselves originated in the Cambrian explosion more than five hundred million years ago, but some of their proteins may be much older.

11
. This number has been estimated as being no greater than 1090. See “Observable universe,” Wikipedia, http://en.wikipedia.org/wiki/Observable_universe, for a more conservative, smaller estimate.

12
. A year has 365 days, and if the universe is on the order of 2 × 1010 years old, winning a jackpot every day adds up to only 7.3 × 1012 jackpots, a ridiculously small number compared to what is needed.

CHAPTER ONE: WHAT DARWIN DIDN’T KNOW

1
. A thorough and well-sourced account of the history of biology through the mid-twentieth century is Mayr (1982). I will cite extensively from it.

2
. See Mayr (1982), 362.

3
. Ibid., 390.

4
. Ibid., 351.

5
. Ibid., 363.

6
. Ibid., 259.

7
. See Whitehead (1978), 39.

8
. This essentialist concept of a species is also sometimes called the typological species concept. See Futuyma (1998), 448.

9
. Successful hybridization that creates new species is not uncommon, especially in plants. See Futuyma (1998). Because bacteria do not reproduce sexually like we do, the concept of a biological species does not apply to them. Nonetheless, they frequently exchange genetic material through a process known as lateral gene transfer, and are thus even more plastic than species of higher organisms. See Bushman (2002).

10
. See Mayr (1982), 304.

11
. I note that
Eupodophis
itself was only discovered recently. See Houssaye et al. (2011).

12
. See Gilbert (2003). Darwin himself made contributions to this area through his extensive studies on barnacles (Cirripedia).

13
. See Mayr (1982), 439.

14
. Aside from his contemporary Alfred Russel Wallace, who proposed a similar theory at about the same time, Darwin is peerless in his radical application of the concept of natural selection, and in the body of evidence he accumulated for its importance. The concept of natural selection existed long before Darwin’s theory, but selection was then usually thought to help eliminate degenerate forms, not to help gradually improve existing forms. See Mayr (1982), 488–500.

15
. See Darwin (1872), chapter 1, page 12.

16
. See Mayr (1982), 710.

17
. Mendel’s laws are summarized in biology textbooks, such as Griffiths et al. (2004). In some Mendelian traits, the offspring of two pure-breeding parents can be intermediate between the parents. The particulate nature of genes can then still be revealed in the second generation, where some individuals display the parental phenotype.

18
. Mendel (1866).

19
. See Kottler (1979), Corcos and Monaghan (1985), Mayr (1982), 728, as well as Schwartz (1999), chapter 7.

20
. See Johannsen (1913). The term
pangene
comes from the word
pangenesis,
the ancient notion that all parts of a body, including eyes, hair, and nails, contribute to inheritance. According to pangenesis, brown-eyed parents, for example, tend to have brown-eyed children because eyes contribute to whatever material a man and woman exchange in procreating. Darwin also believed in pangenesis. See Mayr (1982), 693. We now know pangenesis to be wrong. Not all parts of our body, but only reproductive cells such as oocytes, contribute inherited material to the next generation.

21
. See Mayr (1982), 783.

22
. This statement is usually attributed to de Vries, and I follow this tradition. It is the closing statement in de Vries (1905), 825. However, de Vries does not claim to be the originator of this statement, but attributes it to Arthur Harris without further reference. Harris makes this statement in a little-noted article, where he declares it a quote but does not provide the source. See Harris (1904). The statement has resurfaced periodically in the literature, for example in the title of research papers like that by Fontana and Buss (1994).

23
. Johannsen himself was very careful not to ascribe any physical reality to genes. See Johannsen (1913), 143–46.

24
. The opposite of such discrete inheritance is also called blending inheritance.

25
. Darwin knew about discrete inheritance but did not think it was very important. See Mayr (1982), 543.

26
. Macromutations may be more frequent in plants than in animals. See Theissen (2006).

27
. See Goldschmidt (1940), 391. In the eyes of mutationists, such mutations were much more important to evolution than selection. See Mayr (1982), 540–50.

28
. The story of the peppered moth is one of the oldest and most-cited instances of “evolution in action” that have been observed within a human life span. See Kettlewell (1973), as well as Cook et al. (2012). Haldane showed how even such rapid genetic change does not require very strong selection. See Haldane (1924).

29
. This assessment comes from human diseases, an especially well studied class of traits. About 1 percent of humans are affected by Mendelian diseases that are caused by mutations in single genes, whereas a much larger percentage of the total population is affected by diseases associated with mutations of weak effects in multiple genes, such as hypertension or diabetes. See Benfey and Protopapas (2005).

30
. There are exceptions that prove the rule, such as the phenomenon of polyploidization, where the entire genetic material of an organism becomes duplicated, which can result in major phenotypic change. Many crop plants are polyploids.

31
. See Huxley (1942).

32
. This quote is itself a simplification from the original in Einstein (1934).

33
. See Mayr (1982), 400.

34
. See Morgan (1932), 177. Curiously, Morgan was an embryologist before he became a geneticist. For a broader discussion see Gilbert (2003).

35
. Population genetics and quantitative genetics have gradually become more sophisticated, and allowed that genes contribute in complex nonlinear ways to a phenotype. They also study multivariate phenotypes, phenotypes that cannot be written as single scalar quantities but are represented as vectors. But even these representations cannot encapsulate the true complexity of phenotypes such as the fold of a protein, which is best represented through the atomic coordinates and molecular motions of its amino acids. The formation of this phenotype is completely determined by its genotype, the amino acid chain, yet it is so complex that we still cannot compute it from information in the genotype.

36
. The term
enzyme
itself had been coined already in 1877 by the German physiologist Wilhelm Kühne.

37
. See Stryer (1995).

38
. See Desmond and Moore (1994). The fifth edition is the first to use the phrase “survival of the fittest,” which had been coined by the philosopher Herbert Spencer.

39
. See Mayr (1982), chapter 19.

40
. See Avery, MacLeod, and McCarty (1944).

41
. See Watson and Crick (1953).

42
. For which Max Perutz and John Kendrew would share the 1962 Nobel Prize in Chemistry.

43
. See Benfey and Protopapas (2005). The very earliest techniques to study genetic variation did not yet read DNA directly, but used alternative measures to measure genetic variation, such as the mobility of different variants of a protein in an electric field. See, for example, Lewontin and Hubby (1966).

44
. See Kreitman (1983) and Oqueta et al. (2010).

45
. See Eng, Luczak, and Wall (2007). Such individuals metabolize alcohol more efficiently into acetaldehyde, which causes the undesired side effects.

46
. The notion of a “paradigm shift” leading to incompatible worldviews was immortalized by the historian Thomas Kuhn. See Kuhn (1962).

47
. See Nikaido et al. (2011).

48
. One of several differences between the English alphabet and the molecular alphabet of DNA is that they contain different numbers of letters and each letter thus carries different amounts of information.

49
. The first draft sequence from both the publicly and the privately funded projects was based on DNA not from just one individual but from multiple individuals. In the privately funded project, some of that DNA came from the project leader himself. See Venter (2003).

50
. The diseases he refers to are so-called complex diseases like diabetes, caused by mutations in multiple genes (and influenced by environmental factors such as diet), as opposed to Mendelian diseases, which are caused by mutations in single genes.

51
. Other molecular interactions, such as those between proteins and DNA that help regulate genes, are also important in signaling, as chapter 5 will point out.

52
. Such mathematical descriptions of biochemical systems existed for many decades, since biologists first described enzymes and the rates at which they can catalyze chemical reactions. See Fell (1997). However, in the last decade of the twentieth century, molecular biology embraced such descriptions as essential to understanding biochemical systems in a newly fashionable branch of biology called systems biology.

53
. See Sedaghat, Sherman, and Quon (2002) for a mathematical model of insulin signaling, and Draznin (2006) for some hypotheses of the mechanisms behind insulin resistance. Sanghera and Blackett (2012) discuss some of the genetic complexities of type 2 diabetes.

54
. As many scientists after Darwin have forcefully argued. See, for example, Dawkins (1997).

55
. To my knowledge, the term
genotype-phenotype map
was coined by the Spanish developmental biologist Pere Alberch, who studied macroscopic phenotypes that were too complex to draw this map in molecular detail. See Alberch (1991). However, the idea behind such maps can be found in the work of many others, such as Sewall Wright, one of the founders of the modern synthesis, or the embryologist Conrad Hal Waddington. See Waddington (1959).