Although this is seldom fully appreciated, von Neumann was one of the first to establish a deep link between life and computation. Reproduction, like computation, he showed, could be carried out by machines following coded instructions. In his model, based on Alan Turing’s Universal Machine, self-replicating systems read and execute instructions much like DNA does: “if the next instruction is the codon CGA, then add an arginine to the protein under construction.” It’s not a metaphor to call DNA a “program” — that is literally the case.
Of course, there are meaningful differences between biological computing and the kind of digital computing done by a personal computer or your smartphone. DNA is subtle and multilayered, including phenomena like epigenetics and gene proximity effects. Cellular DNA is nowhere near the whole story, either. Our bodies contain (and continually swap) countless bacteria and viruses, each running their own code.
It’s not a metaphor to call DNA a “program” — that is literally the case.
Biological computing is “massively parallel,” decentralized, and noisy. Your cells have somewhere in the neighborhood of 300 quintillion ribosomes, all working at the same time. Each of these exquisitely complex floating protein factories is, in effect, a tiny computer — albeit a stochastic one, meaning not entirely predictable. The movements of hinged components, the capture and release of smaller molecules, and the manipulation of chemical bonds are all individually random, reversible, and inexact, driven this way and that by constant thermal buffeting. Only a statistical asymmetry favors one direction over another, with clever origami moves tending to “lock in” certain steps such that a next step becomes likely to happen.
This differs greatly from the operation of “logic gates” in a computer, basic components that process binary inputs into outputs using fixed rules. They are irreversible and engineered to be 99.99 percent reliable and reproducible.
Biological computing is computing, nonetheless. And its use of randomness is a feature, not a bug. In fact, many classic algorithms in computer science also require randomness (albeit for different reasons), which may explain why Turing insisted that the Ferranti Mark I, an early computer he helped to design in 1951, include a random number instruction. Randomness is thus a small but important conceptual extension to the original Turing Machine, though any computer can simulate it by calculating deterministic but random-looking or “pseudorandom” numbers.
Parallelism, too, is increasingly fundamental to computing today. Modern AI, for instance, depends on both massive parallelism and randomness — as in the parallelized “stochastic gradient descent” (SGD) algorithm, used for training most of today’s neural nets, the “temperature” setting used in chatbots to introduce a degree of randomness into their output, and the parallelism of Graphics Processing Units (GPUs), which power most AI in data centers.
Traditional digital computing, which relies on the centralized, sequential execution of instructions, was a product of technological constraints. The first computers needed to carry out long calculations using as few parts as possible. Originally, those parts were flaky, expensive vacuum tubes, which had a tendency to burn out and needed frequent replacement by hand. The natural design, then, was a minimal “Central Processing Unit” (CPU) operating on sequences of bits ferried back and forth from an external memory. This has come to be known as the “von Neumann architecture.”
Turing and von Neumann were both aware that computing could be done by other means, though. Turing, near the end of his life, explored how biological patterns like leopard spots could arise from simple chemical rules, in a field he called morphogenesis. Turing’s model of morphogenesis was a biologically inspired form of massively parallel, distributed computation. So was his earlier concept of an “unorganized machine,” a randomly connected neural net modeled after an infant’s brain.
These were visions of what computing without a central processor could look like — and what it does look like, in living systems.
