Swiss Army Knives and 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 statistical learning tools, 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. In statistical learning, dimension reduction methods help make complex datasets easier to visualize, compare, and model. They help to reveal clusters, trajectories, unusual samples, or hidden modes of response that may be difficult to see in the original measurements. The goal is to make the data’s structure more learnable (Cunningham & Ghahramani, 2015; Greenacre et al., 2022).
For those of us working in mycelium technology, and who take seriously the idea that fungal physical plasticity holds potential beyond 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 help reveal the underlying shape hidden in fungal response, the meaning, 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 gesture toward emergent patterns and properties.
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 statistical learning, 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 (Greenacre et al., 2022). 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 (Hinton & Salakhutdinov, 2006). 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, following the network-oriented view of mycelial growth described by Fricker et al. (2017). From these images, you extract a set of quantitative features: hyphal length, number of branch points, node density, anisotropy, fractal dimension, skeletal density. 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 conceptualizing 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 with unknown underlying relationships, 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 is a relational adjustment. Dimension reduction tools, when used with intention, open that possibility. They allow you to perceive the global tendencies of a fungus 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. You start to see what the fungus is; how it moves, how it tolerates, how it adapts. That’s the moment where design with fungi becomes more precise, more meaningful, more collaborative, and ultimately more powerful.
The shift from interpreting individual features to understanding full depth of 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 is 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 is a way of gaining access to physical meaning, enabling creative leverage and developmental stability. They help you hear the fungus more clearly, and respond in kind.
References
Cunningham, J. P., & Ghahramani, Z. (2015). Linear dimensionality reduction: Survey, insights, and generalizations. Journal of Machine Learning Research, 16(89), 2859–2900.
Fricker, M. D., Heaton, L. L. M., Jones, N. S., & Boddy, L. (2017). The mycelium as a network. Microbiology Spectrum, 5(3). https://doi.org/10.1128/microbiolspec.FUNK-0033-2017
Greenacre, M., Groenen, P. J. F., Hastie, T., Iodice D’Enza, A., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), Article 100. https://doi.org/10.1038/s43586-022-00184-w
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647