Book review: How Life Works

In the 1980s, the anthropologist Lucy Suchman studied how office workers interacted with sophisticated photocopiers. What she found was that people’s actions were not determined by predefined plans. Instead, people decided what act to take based on the details of the particular situation they found themselves in. They used predefined plans as resources for helping them choose which action to take, rather than as a set of instructions to follow.

I couldn’t help thinking of Suchman when reading How Life Works. In it, the British science writer Philip Ball presents a new view of the role of DNA, genes, and the cell in the field of biology. Just as Suchman argued that people use plans as resources rather than explicit instructions, Ball discusses how the cell uses DNA as resources. Our genetic code is a toolbox, not a blueprint.

Imagine you’re on a software team that owns a service, and an academic researcher who is interested in software but doesn’t really know anything about it comes to and asks, “What function does redis play in your service? What would happen if it got knocked out?”. This is a reasonable question, and you explain the role that redis plays in improving performance through caching. And then he asks another question: “What function does your IDE’s debugger play in your service?” He notices the confused look on your face and tries to clarify the question by asking, “Imagine another team had to build the same service, but they didn’t have the IDE debugger? How would the behavior of the service be different? Which functions would be impaired” And you try to explain that you don’t actually know how it would be different. That the debugger, unlike redis, is a tool, which is sometimes used to help diagnose problems. But there are multiple ways to debug (for example, using log statements). It might not make any difference at all if that new team doesn’t happen to use the debugger. There’s no direct mapping of the debugger’s presence to the service’s functionality: the debugger doesn’t play a functional role in the service the way that redis does. In fact, the next team that builds a service might not end up needing to use the debugger at all, so removing it might have no observable effect on the next service.

The old view sees these DNA segments like redis, having an explicit functional role, and the new view sees them more like a debugger, as tools to support the cell in performing functions. As Ball puts it, “The old view of genes as distinct segments of DNA strung along the chromosomes like beads, interspersed with junk, and each controlling some aspect of phenotype, was basically a kind of genetic phrenology.” The research has shown that the story is more complex than that, and that there is no simple mapping between DNA segments in our chromosomes and observed traits, or phenotypes. Instead, these DNA segments are yet another input in a complex web of dynamic, interacting components. Instead of focusing on these DNA strands of our genome, Ball directs our attention on the cell as a more useful unit of analysis. A genome, he points out, is not capable of constructing a cell. Rather, a cell is always the context that must exist for the genome to be able to do anything.

The problem space that evolution works in is very different from the one that human engineers deal with, and, consequently, the solution space can appear quite alien to us. The watch has long been a metaphor for biological organisms (for example, Dawkins’s book “The Blind Watchmaker”), but biological systems are not like watches with their well-machined gears. The micro-world of the cell contains machinery at the scale of molecules, which is a very noisy place. Because biological systems must be energy efficient, they function close to the limits of thermal noise. That requires a very different types of machines than the ones we interact with at our human scales. Biology can’t use specialized parts with high tolerances, but must instead make do with more generic parts that can be used to solve many different kinds of problems. And because the cells can use the same parts of the genome to solve different problems, asking questions like “what does that protein do” becomes much harder to answer: the function of a protein depends on the context in which the cell uses it, and the cell can use it in multiple contexts. Proteins are not like keys designed to fit specifically into unique locks, but bind promiscuously to different sites.

This book takes a very systems-thinking approach, as opposed to a mechanistic one, and consequently I find it very appealing. This is a complex, messy world of signaling networks, where behavior emerges from the interaction of genome and environment. There are many connections here to the field of resilience engineering, which has long viewed biology as a model (for example, see Richard Cook’s talk on the resilience of bone). In this model, the genome acts as a set of resources which the cell can leverage to adapt to different challenges. The genome is an example, possible the paradigmatic example, of adaptive capacity. Or, as the biologists Michael Levin and Rafael Yuste put it, whom Ball quotes: “Evolution, it seems, doesn’t come up with answers so much as generate flexible problem-solving agents that can rise to new challenges and figure things out on their own.”