As space exploration geared up in the 1960s, scientists were faced with a new dilemma. How could they recognize life on other planets, where it may have evolved very differently—and therefore have a different chemical signature—than it has on Earth? James Lovelock, father of the Gaia theory, gave this advice: Look for order. Every organism is a brief upwelling of structure from chaos, a self-assembled wonder that must jealously defend its order until the day it dies. Sophisticated information processing is necessary to preserve and pass down the rules for maintaining this order, yet life is built out of the messiest materials: tumbling chemicals, soft cells, and tangled polymers. Shouldn’t, therefore, information in biological systems be handled messily, and wasted? In fact, many biological computations are so perfect that they bump up against the mathematical limits of efficiency; genius is our inheritance.
DNA stores information at a density per unit volume exceeding any other known medium, from hard disks to quantum holography.1 It’s so dense that all the world’s digital data could be stored in a dot of DNA the weight of eight paper clips. This remarkable storage density is paired with an equally remarkable reading mechanism. A developing embryo must self-direct the rapid division, migration, and specialization of its constituent cells based on the information stored in DNA. Cells diverge from one another to grow in different ways, depending on their position in the embryo. This means that precise control of gene expression is necessary both in space and in time. Even minor errors could spell death or deformity for the organism.
The question is, how quickly and effectively can spatial information be communicated in order for development to unfold properly? Alan Turing, the father of modern computing, was fascinated by the idea that life might be reducible to mathematical laws, and tackled this question in the early 1950s. He predicted that the spatial patterning of tissues during embryonic development could be controlled through the concentration of chemical signalers, called morphogens. He derived equations showing that interactions between morphogens that activate and inhibit one another’s gene expression could set up standing waves of morphogen concentration, and control embryonic patterning. With just four variables—production and degradation rates, diffusion rate, and interaction strength—Turing could reproduce biologically plausible, self-directed pattern formation.
His prediction of a chemical system of information management was proven true decades later with the first discovery of a morphogen in fruit flies, called bicoid (though his equations proved to be too simple). Cells in a fruit fly embryo with high bicoid concentration become the fly’s head, and those with lower concentration become its body. What is remarkable, however, is how quickly and accurately these cells differentiate. In 2007, a team led by Princeton biophysicist Thomas Gregor measured the concentration gradient and diffusion rate of bicoid in fruit fly embryos. They estimated that it would take around two hours for embryonic cells to measure the morphogen concentration with enough precision for adjacent cells to mature differently (measurement precision increases with measurement time). But this is nearly the entirety of the fruit fly’s developmental period, from fertilization to cellularization. The embryo was developing faster and more precisely than should have been possible.
Gregor proposed that multiple cells could be sharing information with each other using a second signaling chemical: One candidate is a morphogen called hunchback. This would allow them to compute a spatial average of bicoid concentrations, rather than relying entirely on individual readings. The averaging process would be less susceptible to variation and noise, and would allow the required pattern accuracy to be achieved in about three minutes, rather than two hours.
The process is not just ingenious, but efficient. To find out how efficient, physicist William Biale