Swiss Army Knives & Finding Meaning in Fungal Physicality

I love the Swiss Army knife. It’s been in my back pocket next to my wallet for the last 20 years (the Victorinox Swiss Champ, to be precise). It’s the kind of tool that earns its place by being perfectly optimized to the right combination of versatility and accessibility. As you learn how and when to use each blade, saw, screwdriver, or awl it stops being just a collection of tools; it becomes an implement for engaging unexpected complexity with confidence. I’ve come to feel the same way about certain tools in data science, in particular dimension reduction tools. Dimension reduction is a way of simplifying complex data by finding the most important patterns, so instead of looking at many measurements separately, you can understand the big picture with just a few meaningful signals.

For those of us working in mycelium technology, and who take seriously the idea that fungal physical plasticity holds potential, and not just variation, dimension reduction tools (like PCA and autoencoders, as examples discussed below) can function like a Swiss Army knife for navigating and making use of that complexity.

These methods don’t just reduce complexity, they help reveal the underlying shape of meaning hidden in fungal response, offering a kind of vocabulary that lets the fungus speak. And once you can see structure, continuity, deviation, recurrence, you are no longer trying to control or work against complexity, but learning from it. That shift is central to turning data into insight, and fungal plasticity into a design space.

Like learning a dialect this is, at its core, a linguistic act. When we treat mycelial growth and physicality as a form of language containing structure, nuance, and variation, we begin to see that dimension reduction tools aren’t just statistical conveniences, they are quite literally translators. They allow the mycelium engineer to extract signals from form, to map fungal expression into structured meaning. Like learning a dialect, they help us recognize which variations are noise, which are emphatic, and which whisper toward emergent patterns and properties. And once you’ve gained even a rudimentary fluency, you’re no longer guessing at what multi-dimensional fungal response might mean, you’re reading it.

PCA (Principal Component Analysis) and autoencoders are two powerful dimension reduction multi-tools in this translation process. It’s worth noting that dimension reduction is a wide-reaching and technically rich area of data science, with a multitude of techniques tailored to different data structures, goals, and assumptions. But for the sake of this discussion, and based on their broad practical usefulness, we’ll focus on PCA and autoencoders within the context of fungal growth and physicality. PCA is a linear method that seeks to find the simplest axes along which variation in a dataset is most pronounced; flattening high-dimensional measurements into a few meaningful, learnable patterns. Autoencoders, by contrast, are neural networks trained to compress and reconstruct data; they’re capable of detecting non-linear, dynamic manifolds representing hidden structures in the feature space. Both of these tools seek to discover the underlying structure within data that may provide a view of the deeper underlying meaning; the true dimensions of responsiveness. In practice, both tools allow the mycelium engineer to move from dozens (or hundreds) of features to a compact set of meaningful dimensions revealing clusters, anomalies, developmental trajectories, or modes of response. They are, in essence, high-dimensional listening devices. And in my experience, they are essential tools for converting the rich but difficult to reconcile vocabulary of fungal physicality into something you can learn from and design with.

Imagine you’ve run a series of micrograph-based assays to evaluate hyphal network morphology across several candidate fungal isolates (see Fricker et al. The Mycelium as a Network). From these images, you extract a set of quantitative features: hyphal length, number of branch points, node density, anisotropy, fractal dimension, skeletal density (the virtues of feature extraction prior to dimension reduction will be a topic for a future article). Each of these features captures a distinct aspect of how the hyphae grow and organize themselves in physical space. On their own, these features tell you something, but not everything. It’s when you begin to treat them as coordinates in a shared feature space—what we might call a response space—that their meaning deepens. A response space is a way of visualizing how an organism or system behaves across many variables at once, like mapping its full range of possible reactions into a single, structured landscape. By applying dimension reduction you can begin to see how different strains may occupy distinct regions of this space, or how a single strain shifts position under changing conditions. You might find that two strains differ not because of any one feature, but because of how a combination of branching frequency, skeletal density, and anisotropy co-vary, which may only become visible when considered as a multidimensional structure. The same logic applies whether you’re analyzing morphology, a suite of mechanical features, textural or aesthetic qualities, or growth kinetics; as long as you’re working with many features that interact, these tools let you compress complexity and visualize response as something whole, structured, and learnable.

This ability to compress, interpret, and respond to global patterns in fungal physicality isn’t just a technical upgrade; it’s a relational shift. In a previous article, I wrote about forming relationships with fungi, and how that process demands going beyond observation to conversation. Dimension reduction tools, when used with intention, open that possibility. They allow you to perceive the global tendencies of a fungus, not as scattered metrics, but as coherent behavior; as a whole organism expressing itself through space and time. When you begin to see a strain’s breadth of responsiveness not just as more or fewer branches, but as a movement across a known response space, something changes. You stop reacting to isolated features and start recognizing patterns of character. And in that recognition lies the conversational capacity for forming relationships. You start to see not just what the fungus does, but who it is; how it moves, how it tolerates, how it adapts. That’s the moment where design with fungi becomes not only more precise, but more meaningful, more collaborative, and ultimately more powerful.

The shift from interpreting individual features to understanding full morphological response has profound implications for design practice. When you can work with the totality of fungal physical responsiveness, you reduce your exposure to unobserved variation. You stop tuning one feature at the risk of destabilizing others. It becomes possible to anticipate and manage the side effects of optimization, to see where targeting one outcome might provoke trade-offs in others. In this sense, interpretation of global fungal responsiveness isn’t just about insight but about risk mitigation. It’s a way of designing with greater confidence, because you’re no longer flying blind to the deeper structure of fungal response. You're building with awareness of the whole organism, not just the part that happens to be convenient to observe.

That’s why I return to the Swiss Army knife. Not just as a metaphor for versatility, but as a reminder that powerful tools earn their value by being compact, adaptable, and close at hand when complexity shows up. Dimension reduction is exactly that kind of tool in a mycelium designer’s statistical tool box. They find vectors of meaning, they cluster, reveal outliers, highlight correlations, uncover hidden structures, and give you practical footholds in data that would otherwise be overwhelming. They are, in effect, Swiss Army knives capable of helping you read, navigate, and act within the vast design space of fungal physicality. Learning to use them isn’t just a technical exercise; it’s a way of gaining access to physical meaning within your organism, enabling creative leverage and developmental stability. They help you hear the fungus more clearly, and respond in kind.