Note — Sep 25, 2022

Fermented Code: Modelling the Microbial Through Miso

If you look on the website you’ll see that I tagged this piece with complexity, ecosystems, systems, trees, nature, fungi, fermentation, microbiome, synthetic biology, and biology. Some of these overlap and are there for findability but still, I rarely use that many tags on an article, which just goes to show the breadth of what Claire L. Evans wrote here.

From a missed batch of home-fermented miso, Evans considers all of the above topics, jumping from nature to coding, trees to models, and more besides. She shows how models (code, a selection of microbes) are used to understand parts of more complex systems, knowing full well that they don’t represent the whole thing, the whole map. Each model (or each miso culture) is a subset of a larger systems, and what is taken out or ignored is as impactful as what is included.

Wonderful piece from one of my favourite writers of the last couple of years. Beyond the actual topic and sub-topics, it’s also a great example of looking to history, looking to nature, looking beyond humans, to find inspiration, questions, and hopefully solutions.

More → When microbes and fermentation are mentioned, I feel I have to also link to Robin Sloan’s great books Sourdough, and The Suitcase Clone (further down the same page).

[A] growing awareness that the solutions to the future’s most intractable problems would not be found exclusively in silicon chips or Silicon Valley, but by drawing inspiration, as Katz and others suggest, from the webs of life beneath our feet, in our gardens and in our guts. […]

My miso, then, is already a kind of model – one good enough to eat. It is a semi-natural interface between the artificial simplicity of a lab system and the dizzyingly diverse communities of microbes flourishing outside my kitchen door. Fermentation is a way of capturing the complexity of microbial ecology, literally drawing it from the air, rendering it knowable, and ultimately domesticating it. […]

There will always be something a model misses or ignores. This is by design. Making a model requires elision: choosing between what is meaningful and what is merely noise. This is complicated by the fact that what appears to be noise at one scale can look like a signal at another. […]

Recent research in ‘unconventional computing’ suggests that we might do well to skip the model entirely and turn to the natural world itself as a computing substrate, building computers from slime mould, fungal mycelium, seedlings, and perhaps entire ecosystems. […]

I think of it as a reminder of all that is still unknown, and unknowable: that which exists beyond the model’s frame.