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

the moral, we have to understand the whole story—but also because our brains are not well practiced in reading chemical language. Fortunately, we can program the artificial intelligence of computers to assist us.
    A genotype tells us which reactions a metabolism can catalyze, the molecules these reactions consume, and the molecules they produce. To decipher its meaning, we would first need to know which nutrients are available—without the right ingredients, you cannot bake a cake—and whether the metabolism can use them to build an essential biomass molecule such as tryptophan. This is easiest for the austere minimal environments where survivalists like
E. coli
can thrive, because they contain so few nutrients, sometimes only a single sugar that provides all the carbon and energy the organism needs.
    Starting from the available nutrients, we would then write a list of all the molecules the metabolism’s reactions
produce
from the available nutrients, find the reactions in the genotype that
consume
these product molecules, and list
their
products, iterating in this way until we find one or more reactions whose products include tryptophan. If no such reaction exists, then the metabolism cannot produce tryptophan.
    FIGURE 6. Metabolic phenotypes
    We would then move on to another biomass molecule, perhaps another amino acid, or one of the four DNA building blocks, repeating the entire procedure for each of the building blocks to find out whether the metabolism can manufacture it. Only when
all
essential biomass molecules can be produced is it viable. 32
    All of this is done on computers, because computation—done right—is faster, cheaper, and can even be better than experimentation. But as the saying goes, the map is not the territory, and we biologists do not fully trust any computation until we can check it. So like a factory that spot-checks its output randomly, we expose organisms with known metabolic genotypes to known chemical environments, and wait, somewhat ghoulishly, for them to grow or die. This has been done, for example, to several hundred mutant
E. coli
strains, each of them engineered to lack one enzyme, and it shows that their computed viability is highly accurate—it is correct for more than 90 percent of strains. 33
    Most biologists who know about this computation think of it as ordinary and do not dwell on how remarkable it is. But more than just remarkable, the capacity to compute viability is profound and revolutionary, a legacy of a hundred years of research in biology and computer science. Darwin and generations of biologists after him could not even dream of it, yet it is crucial to understanding metabolic innovability—nature’s ability to create new metabolic phenotypes.
    This computation works for any organism whose metabolism we know, and for any known chemical environment, whether Arctic soil, tropical rain forest, oceanic abyss, or a mountain meadow. It also applies to any aspect of a metabolic phenotype—to any molecule a metabolism
could
make. But among all these aspects, viability is the most fundamental, and new methods of making biomass and using chemical fuels are by far the most important innovations. They are also the most far-reaching, opening new territories to life and its metabolic engines.
    The reason for the importance of fuel innovations is simple: The world changes all the time, and no matter how successful a metabolism is
today,
it will almost certainly become unsuccessful at some point in the future, like an economy that depends on exhaustible fossil fuels. Chemical environments always change as consumed nutrients ebb and new foods flow. Organisms that depend on a single, specific combination of nutrients are evolutionary dead ends, and ongoing innovation is needed to survive. 34 Fortunately, many different kinds of molecules
can
provide energy and chemical elements like carbon. Some are as familiar as glucose and sucrose, others less so, like the poison