Mycelium as a Function of Nested Volumes
Scaling and Eccentricity as a Function of Volume
Scaling mycelium production in solid-state fermentation systems is a negotiation between biology, physics, and design, where each step-change in volume multiplies complexity. In these systems, scaling is about understanding how product and process eccentricity evolve as volume grows.
By comparison, submerged cultivation offers a more direct path. In liquid systems, though certainly not simple by any stretch of the imagination, understanding specific growth rate and biomass kinetics can be more straightforward. Conversion rates and growth conditions can be measured continuously, and feedback control is accessible through sensors and flow systems. Even morphology can be influenced more directly through mixing, shear, and control over fungal propagule density and form, giving the operator direct physical levers to manage the shape and distribution of developing biomass (Lu et al., 2024).
Solid-state systems are less forgiving in granting these same conveniences. Bulk mixing has distinct challenges and often is inaccessible given process goals and operational assumptions. Optical access and continuous monitoring can be more difficult. Gas and moisture transfer occur through porous and uneven pathways that shift over time and are difficult to monitor. Growth heterogeneity emerges from the local structure of the substrate, and measurement often requires destructive sampling. These realities, combined with the familiarity and infrastructure built around submerged systems, often drive process design to default toward liquid cultivation even when it may be less suitable from a scale and economic perspective (Mitchell et al., 2006). The irony here is that the mycelial fungus, in a solid-state context, is operating in a more natural environment than in submerged systems and is leveraging its physical vocabulary to do so (from which we derive value), yet this same naturalism increases the challenge.
The transition from a few grams of substrate to kilograms or cubic meters introduces not only physical gradients but also the opportunity for behavioral and physical divergence. As volume increases, so does the opportunity for conditional and operational variance, and fungal growth rationally tracks these shifts. What may appear as irregularity may actually reflect stability; a coherent alignment of metabolism, morphology, and spatial investment with the changing conditions around it. This eccentricity can manifest across different facets of scale that describe the total operational volume:
As variance in colonization within a reactor.
As shifts in physicality or efficiency across environmental parameters.
As operational variability between production systems.
Understanding and managing this eccentricity is at the center of scaling mycelium. It is both a technical problem and a way of thinking. Success in solid-state fermentation depends on learning how the fungus distributes itself in space, where “space” extends beyond physical dimensions to include the parameter space that defines growth conditions, and the operational space that spans systems and scales. In this sense, scaling means understanding how mycelium behaves within nested volumes, not just a larger container.
Eccentricity Through Physical Space
When scaling solid-state fermentation, the first form of eccentricity encountered is within the physical volume of the container. The fungus expands through gradients of moisture, oxygen, density, and temperature that evolve as metabolism proceeds. These gradients are defining features that shape how the fungus distributes biomass, reinforces structure, and negotiates its environment.
At small scales the architecture is more manageable. Gas exchange is relatively uniform, moisture stable, and temperature gradients minor. As volume increases the substrate becomes a landscape; air pathways become obscured with hydration or colonization, thermal pockets form in active regions, and water potential gradients create corridors of higher or lower activity. The mycelium responds by redistributing growth and metabolism to track this uneven terrain, creating a dynamic feedback loop.
The literature on solid-state bioreactors reflects this clearly. Temperature shifts of only a few degrees can redirect metabolism, and small differences in substrate packing can alter oxygen availability by orders of magnitude. Mitchell, Berovič, and Krieger describe how even modest bed thicknesses introduce nonlinearities in gas and heat transfer that influence productivity (Mitchell et al., 2006). From the fungal perspective, these are ecological signals rather than disturbances that drive differentiation in tissue density, pigmentation, and texture.
Recognizing spatial heterogeneity as behavioral expression reframes design priorities. What may appear as instability, such as a denser mat near the surface or a sparser network below, may reflect a coherent redistribution of effort to maintain function under shifting conditions. The goal is to manage and interpret this eccentricity, ensuring that the fungal response space remains within a practical envelope of function.
Effective scale-up begins with recognizing these physical gradients and accepting their persistence as a natural feature of the system. The fungus perceives its substrate as a heterogeneous landscape of opportunity and constraint. At large scales, design becomes architectural, guiding environment and structure to influence fungal spatial logic. This is why solid-state bioreactors are designed around spatial concepts (trays, columns, and tunnels), each representing an attempt to choreograph mass transfer.
For the mycelium engineer, growth distribution is a spatial dialogue between organism and environment. The pattern of colonization is a record of interaction, showing how the fungus interprets its context. Appreciating spatial eccentricity as informative asymmetry, as the organism expressing its experience of the environment, builds the foundation for scaling with understanding rather than resistance.
Eccentricity Through Parametric Space
If spatial eccentricity describes how the fungus distributes itself through a physical volume, parametric eccentricity describes how it behaves across a volume of conditions. Every process variable (temperature, humidity, airflow, nutrient concentration, inoculum quality, strain lineage) defines an axis within a multidimensional parameter space. This volume is governed by design intent as well as the physical and operational constraints of the system itself.
At laboratory scale this space can be explored freely. As systems scale up, the parameter space becomes less a matter of choice and more a balance between what is designed and what the equipment and process architecture can actually maintain. Within this blended space, fungal eccentricity reflects both its adaptive logic and the boundaries of engineering control. Growth patterns shift with both environmental factors and the mechanical realities of the process.
Understanding this coupling is essential, and it demands more than static measurement. Quantitative tools can help translate this moving biological complexity into patterns that can be learned from. Design of experiments provides a structured way to test several process variables at once, allowing researchers to see which variables matter and how they interact (Montgomery, 2017). Dimensionality reduction helps simplify complex data by taking many measured features, such as density, texture, growth rate, color, moisture, or mechanical performance, and reducing them into a smaller number of meaningful patterns (Jolliffe & Cadima, 2016). This allows the mycelium engineer to see the larger shape of the response without becoming lost in dozens of separate measurements. Response surface modeling helps map how fungal outcomes change across a range of conditions, showing where performance improves, where it declines, and where useful but narrow regions of behavior may exist (Kalil et al., 2000; Myers et al., 2016). Feature importance analysis helps identify which inputs or measured features most strongly influence a model’s predictions, giving the researcher a clearer sense of which parts of the system carry the most explanatory weight (Breiman, 2001; Hastie et al., 2009).
Statistical process control, or SPC, provides a way to observe the system as a living signal over time, connecting individual measurements into a larger picture of process behavior (Montgomery, 2019). Control charts are time-based plots that show whether a process is behaving within its expected range or beginning to move outside of it (Montgomery, 2019). Trend analysis looks for gradual directional change, such as a process slowly drifting warmer, wetter, slower, denser, or less uniform over repeated runs (Hunter, 1986; Page, 1954). Multivariate process monitoring extends this logic by watching many variables together, recognizing that fungal behavior often changes through coupled movement instead of through a single variable acting alone (Kourti & MacGregor, 1995). For example, a shift in moisture may only become meaningful when paired with a shift in airflow, temperature, or colonization rate. Together, these tools help identify when variability represents natural fungal adaptation and when it signals process drift, equipment imbalance, or loss of operational control (Kourti & MacGregor, 1995; Montgomery, 2019; Peña y Lillo et al., 2000). By tracking data over time, SPC converts variation into structure, revealing the shape of the parametric volume as it evolves through operation.
Parametric eccentricity is thus the expression of fungal flexibility across both intentional and unavoidable variability. The goal of control is to understand its character and detect which forms of eccentricity are the fungus rationally tracking its context, and which indicate an underlying shift in system balance. In this sense, process control becomes less about maintaining a point and more about maintaining awareness of how the organism moves within its own parametric landscape.
Eccentricity Through Operational Space
At the operational level, eccentricity appears across runs, reactors, and facilities. Even when parameters are well controlled and conditions appear identical, the fungal capacity to perceive and respond to subtle environmental variation often exceeds the practical thresholds of engineering control. Airflow, temperature gradients, or moisture distribution that fall well within acceptable limits for equipment performance can still present meaningful differences to the organism. What emerges is less a single process repeated and more a family of processes linked by shared intent and bounded variability. This is operational eccentricity: the measure of how system stability holds under repetition.
In principle, reproducibility means that two runs under equivalent conditions yield equivalent outcomes. In practice, solid-state fermentation complicates this. The distributed nature of growth, combined with substrate heterogeneity and environmental noise, ensures that no two runs are truly the same. Small differences in substrate packing, inoculum age, airflow, or temperature stratification can cascade into variation in product qualities.
Rather than treating this as failure, we can view operational eccentricity as a diagnostic property of the system. Stability is not the absence of variability but the ability of the process to express variability within practically useful bounds. Statistical process control (SPC) provides a framework for quantifying this behavior (Montgomery, 2019). By tracking key variables over time, control charts and multivariate monitoring distinguish between natural variation, which reflects the system’s operational rhythm, and assignable variation, which signals drift or systemic error (Kourti & MacGregor, 1995; Montgomery, 2019). This becomes a behavioral map of the process, capturing how the system moves when disturbed and revealing where the forces driving that movement may lie.
Reproducibility at scale depends on understanding this movement. A well-characterized process defines, rather than eliminates, its boundaries. Control limits grounded in biological reality allow confidence in performance even as runs deviate within expected ranges. True operational maturity comes when teams recognize which deviations represent rational fungal responses and which indicate imbalance in equipment or practice. Operational eccentricity is both a biological and organizational property, reflecting the resilience of the fungus and the discipline of the process that surrounds it.
Learning Through Nested Volumes
Beyond the pursuit of process and product uniformity, scaling mycelium is the study of how fungal outcomes take shape across nested volumes. Each layer of scale (physical, parametric, and operational) introduces its own form of eccentricity, with fungal feedback shaped as much by the organism’s adaptability as by the boundaries of control. The aim is not narrowly to reproduce a known outcome, but to design systems that sustain a functional dialogue with this eccentricity across the full dimensionality of scale. Scaling, then, is about understanding how biological and functional integrity are maintained across changing conditions, and how coherence persists through variation.
References
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
Hunter, J. S. (1986). The exponentially weighted moving average. Journal of Quality Technology, 18(4), 203–210. https://doi.org/10.1080/00224065.1986.11979014
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), Article 20150202. https://doi.org/10.1098/rsta.2015.0202
Kalil, S. J., Maugeri, F., & Rodrigues, M. I. (2000). Response surface analysis and simulation as a tool for bioprocess design and optimization. Process Biochemistry, 35(6), 539–550. https://doi.org/10.1016/S0032-9592(99)00101-6
Kourti, T., & MacGregor, J. F. (1995). Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemometrics and Intelligent Laboratory Systems, 28(1), 3–21. https://doi.org/10.1016/0169-7439(95)80036-9
Lu, Z., Chen, Z., Liu, Y., Hua, X., Gao, C., & Liu, J. (2024). Morphological engineering of filamentous fungi: Research progress and perspectives. Journal of Microbiology and Biotechnology, 34(6), 1197–1205. https://doi.org/10.4014/jmb.2402.02007
Mitchell, D. A., Berovič, M., & Krieger, N. (Eds.). (2006). Solid-state fermentation bioreactors: Fundamentals of design and operation. Springer. https://doi.org/10.1007/3-540-31286-2
Montgomery, D. C. (2017). Design and analysis of experiments (9th ed.). Wiley.
Montgomery, D. C. (2019). Introduction to statistical quality control (8th ed.). Wiley.
Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: Process and product optimization using designed experiments (4th ed.). Wiley.
Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1–2), 100–115. https://doi.org/10.1093/biomet/41.1-2.100
Peña y Lillo, M., Pérez-Correa, R., Latrille, E., Fernández, M., Acuña, G., & Agosin, E. (2000). Data processing for solid substrate cultivation bioreactors. Bioprocess Engineering, 22(4), 291–297. https://doi.org/10.1007/s004490050735