Arrival of the Fittest: Solving Evolution's Greatest Puzzle by Andreas Wagner

impossible. But these odds change if a crowd of readers can browse the library along a genotype network that extends far through the library. Because genotype networks are so large, the population could explore thousands of neighborhoods and increase the odds of finding the new lifesaving phenotype.
    You may have noticed a hidden premise here: Different neighborhoods must contain different novel phenotypes. Near a volume on photovoltaics, you would find others on medieval French literature, twentieth-century architecture, and Italian cooking, whereas near
another
volume on photovoltaics—in a different part of the library—books on toy trains, World War II, and astrophysics would be shelved. In metabolic terms, you might find metabolisms viable on acetate and ethanol and citrate in one neighborhood, and metabolisms viable on sucrose and fructose in another.
    To find out whether this bizarre library organization really exists, we chose pairs of metabolic texts that had the same phenotype (viability on glucose) but that were otherwise very different. The two metabolisms, A and B, were located in different parts of the library—they did not share many reactions—yet both were part of the same genotype network. We then examined the phenotypes of all their five-thousand-odd neighbors, and found that some of them were likewise viable on glucose—they belonged to the same genotype network—while others had lost a critical chemical reaction, which spells death. Yet other neighbors—those we were really interested in—could live on a new combination of fuels, such as ethanol or fructose. For these networks we asked: Do the neighbors of metabolic genotype A—those texts that differ from A in only a single reaction—contain metabolic innovations different from those of the neighbors of metabolic genotype B? If the neighborhood of A contained metabolisms viable on the new fuels ethanol and fructose, would the neighborhood of B contain metabolisms viable on, say, acetate and sucrose?
    After analyzing thousands of network pairs, and after studying phenotypes involving eighty different fuel molecules, we had found that the premise was correct. Different neighborhoods contain texts with new meanings, but these meanings differ between neighborhoods. Most metabolic innovations are unique to one neighborhood and do not occur in the other. (Because each new phenotype has its own genotype network, this also means that different genotype networks in the library are interwoven in an unfathomably complex way.)
    We then went one step further. With our computers’ help, we wandered once again through a genotype network in the metabolic library, except that now we behaved like inventory clerks with their notepads, listing all the innovations in the immediate neighborhood of our path—all innovations that were within easy reach. We listed all the different new phenotypes in the walker’s neighborhood before the start of the walk, and examined the neighborhood again after the first step. If it contained a new phenotype that was not already on the list, we added it to the list, took one further step, examined the new neighborhood, added any new phenotypes, and so on, for thousands of steps. Because we knew that different neighborhoods contain different innovations, we expected the list to grow over time, as new phenotypes became accessible. But we expected that we would run out of new phenotypes eventually.
    Wrong. Long after our notepads were full, we were still encountering innovations.
    Worried that this trip had yielded an unusually rich bounty, we went on many more trips, from different starting points in the library, metabolisms viable on different fuel molecules. And we also crowdsourced our shopping, exploring the library not with a single metabolism but with entire populations of evolving metabolisms to tally how many different new phenotypes they found. In every instance, innovations continually piled up, with no sign of slowing