Embracing Fungal Complexity in Mycelium R&D
The wonderful thing about the mycelium technology space, especially in designing whole-thallus products, is that people often come from diverse backgrounds: industrial design, food science, biology, materials engineering, architecture, the arts. Working in mycelium product and process R&D requires blending elements of mycology, process engineering, design, systems thinking, materials science, food science, and data science. As a result, it’s not always intuitive which skill sets or frameworks are most essential.
I’ve come to recognize a set of organizing principles that is far from comprehensive, but I believe can be generalized to most mycelium development scenarios and sits at the foundation of working effectively with fungal systems.
1. Physical (phenotypic) plasticity is the heart of designing with filamentous fungi and defines both the critical opportunity and greatest challenge. The opportunity lies in being able to engineer diverse multi-feature outcomes from a wide range of addressable inputs, but can also make processes uniquely difficult to control and scale. Fundamentally, physical plasticity derives from the basis of filamentous growth itself, where dynamic decision making in physical distribution is a strategy for increasing the probability of continued resource acquisition while maximizing efficiency. There are so many powerful contributors to the mycological literature, but personally I’ve found that authors like David Moore, Mark Fricker, and N.P. Money have laid out foundational frameworks and language for practically comprehending and applying fungal physical plasticity.
2. Designing with fungi, then, is not about controlling a single variable or targeting a single response feature, but rather about embracing and working within a high-dimensional system. Fungal responses are inherently multidimensional and interdependent, with each phenotype reflecting the influence of multiple interacting parameters. In practical R&D terms this leads to truly immense solution spaces. To navigate this dimensionality we need a technical toolkit that can handle complexity without becoming overwhelmed by it. That means becoming comfortable with featurization, feature engineering, dimension reduction, and feature importance, not just as data science techniques, but as ways of translating the physical language of fungi into learnable and practically manageable signals that capture the depth of physical responsiveness.
3. The breadth of input-response spaces means adopting efficient and responsive experimental design, like adaptive design and learning-based approaches that can maximize insight and distance of actionable progress per experiment, recognizing that single factor and full factorial experiments are rarely the most efficient options. And crucially, it means learning to separate the speed of experimentation from the speed of learning. Leveraging uncertainty reduction and model confidence as signals can help prevent futile searches by showing when we are gaining understanding, and more importantly when we are not.
Designing with and within fungal complexity is not optional – it’s foundational – driven both by the excitement of what can be mined from fungal physicality and by a respect for how easily bad conclusions, futile searches, and failed programs can emerge in such a complex development space. Bringing together the language of mycology and phenotypic plasticity with a robust learning toolkit isn’t just about being a more effective mycelium engineer but about showing respect for the organism, for the practice, for the potential of the industry, and for my colleagues.