In 2020 I had a fascinating conversation with Michael Levin, a professor whose work seeks to “detect, understand, predict, and communicate with truly diverse intelligences, including cells, tissues, organs, synthetic living constructs, robots, and software-based AIs.”
Michael works with a menagerie of very different living systems, looking at how different types of signals and interactions lead to intelligence. In a conversation full of surprises about cutting worms in half, growing back limbs, and creating robots out of frog cells, I was particularly struck with a much more mundane point about the metaphors guiding work in his lab.
Metaphors shape how we think about the world, which in turn shapes the science that we do. For decades, the metaphor of DNA as a code that “programs” cells like software has driven a huge amount of work in biology forward. But for Levin and his lab, DNA isn’t software:
I don’t think DNA is the software. Not that only one metaphor is valid, but the one that we have found useful is this idea that the real-time physiology of the organism is the software. My background is in computer science, so I come at this from more of a computational perspective. The important thing about software is that if your hardware is good enough — and I’m going to argue that probably all life at this point is good enough — then the software is rewritable. That means you can greatly alter what it does without meddling with the hardware.
People are very comfortable with this in the computer world. When you switch from Photoshop to Microsoft Word, you don’t get out your soldering iron and start rewiring your computer, right? In fact, that is exactly how computation was done in the 1940s, and I think that’s where biology is today. It’s all about the hardware. Everybody’s really interested in genomic editing and rewiring gene regulatory networks. These are all important things, but they are still very close to the machine level. In our group, we think of the DNA as producing cellular hardware that is actually implementing physiological software, which is rewritable. That means you can greatly alter the behavior of the system without actually having to go in and exchange any of the parts. I think that’s the amazing thing about biology. The plasticity is quite incredible.
Hearing Michael talk about DNA in this way, as the hard-coded genomic defaults vs. a more flexible physiological signal, felt immediately both jarring and generative, shocking me out of the complacency of the code metaphor. The idea of DNA as software had once been surprising, activating a vibrant community of weirdos to work with biology. Hearing such a different perspective made me realize that perhaps the idea of DNA as code had lost its sparkle.
Evelyn Fox Keller (one of my all-time faves when it comes to exploring metaphors and language in biology) wrote about what it takes for metaphors to be dynamic and empowering for new kinds of thinking in her paper on the cognitive functions of metaphor in the natural sciences (hat tip to Joseph for pointing me to this most recent work of hers). Metaphors lose their power when they are taken at face value. When DNA just is software in our minds, we can’t stretch to uncover new truths:
As Hesse writes, what is lost in the effort to reduce metaphor to simple analogy or paraphrase are “the elements of surprise, tension, and creativity [that] are essential to the metaphor but missing from the paraphrase. Everyone recognizes a distinction between live and dead metaphor: metaphor is interesting only when it is alive – provoking surprise and shock, indicating new thought”. Indeed, the essence of a live metaphor is precisely the juxtaposition of similarity and difference, the manifest untruth of equating source and target. And what gives it its value in the logic of scientific discovery is precisely the instability it generates by virtue of its insistence on both similarity and difference, its insistence that, at one and the same time, man both is and is not a wolf. Lose this duality, and one loses the vitality of the metaphor.
There is opportunity for new dynamism at the intersection of computers and biology when we look more deeply at software itself and see programming with fresh eyes through different metaphors. In the effort to make biology into a “true” engineering discipline, perhaps we’ve missed some of the vitality and messiness of how technologies are themselves built, and the opportunity in that messiness. In a critique of synthetic biology’s foundational stories of engineering approaches, Jamie Davies highlights how software itself has a more biological character than we might at first think. If DNA is hard wired in individual organisms, the way it changes over time in a population perhaps looks a bit more like the rapid development of software:
Of all the engineering disciplines, software is the most like biology anyway because it shares the attributes of rapid, almost cost-free reproduction and of very large numbers of variants being able to co-exist and compete in an ‘ecosystem’. However, given that one use of software is to conduct the predictive modelling at the beginning of the classical engineering process, it would be perfectly possible for this evolutionary, exploratory way of working to be used to generate and evaluate many models of a desired object (a submarine hull, say) and to find an optimum plan, even if the engineers do not understand explicitly why it is the optimum. The idea has been used to design optimum wiring networks and the ‘evolved design’ of NASA’s ST5 spacecraft antenna is an example of a plan that arose this way. The use of a ‘biological’ rather than conventional method of working on this project, a micro-satellite in which efficiency was very important, is telling, and very relevant to the restraints of synthetic biology.
Of course the biologizing of software only continues with the explosion of AI and machine learning. People speak of the largest models as natural phenomena that need to be studied experimentally in order to understand how they behave the way they do. The metaphors around code, data, and intelligence today are all extremely alive, constantly under debate and up for grabs. This liveliness can seem problematic, pulling us into technological dead-ends—DNA isn’t actually software after all…—but metaphors are inescapable, and that’s ok. Erika Szymanski, a rhetorician who studies how language shapes the development of biotechnologies and human-microbe interactions writes that the trouble with metaphors is that they don’t go away:
It might be argued that scientists should attempt to escape the constraints of metaphorical language altogether, but this is neither possible nor desirable. DNA cannot be explained as it “really is” because knowledge about DNA does not exist outside of language, and all language is metaphorical in describing things in terms of their selective resemblance to other things.
We can’t escape metaphors. Let’s at least keep them alive.