The article, written by Priscilla Zwiercheczewski of the University of Mons in Belgium and Joyce Siqueira of the Federal University of Pernambuco in Brazil, examines a paradox at the heart of the circular bioeconomy. Policy documents and strategic roadmaps celebrate microorganisms as key enablers for closing carbon cycles, recycling waste and replacing fossil-derived inputs. Yet many implementations remain centered on feedstock substitution rather than system redesign. Biofuels and bioplastics frequently aim to swap fossil carbon for biomass-derived inputs while leaving production chains and end-of-life pathways largely unchanged. The result, the authors contend, is a circular narrative evaluated with linear criteria, a mismatch that confines microbes to converting inputs into products within a pipeline and keeps even advanced fields such as precision fermentation largely product-centric.

The dominant logic of industrial relevance is easy to recognize. Desirable strains exhibit high product yield and productivity, robust performance under specific process conditions, genetic tractability and compatibility with existing reactor infrastructure and downstream processing. In practice, this profile is embodied by a small group of domesticated hosts, including Escherichia coli, Saccharomyces cerevisiae, Corynebacterium glutamicum and Bacillus species, which are repeatedly optimized and standardized through design-build-test-learn cycles, high-throughput phenotyping pipelines and machine learning-guided workflows. Even when discussions broaden to include robustness, stability or risk reduction during scale-up, the underlying representation remains production-centered: microbial systems appear as single-input, single-output units in process flow diagrams, with performance collapsed into titers, rates, yields and cost per unit of product.

That representation creates systematic blind spots. The ecological complexity that characterizes multispecies communities, including division of labor, cross-feeding, niche complementarity and long-term resilience, is difficult to translate into standard metrics and therefore rarely enters formal decision criteria. Wild strains and naturally assembled consortia, which evolved to operate in heterogeneous, fluctuating environments and to distribute functions across partners, often appear noisy or inefficient when forced into pure-culture, fixed-input, single-product processes, even when their native interaction patterns would be advantageous at the scale of a regenerative system. Microorganisms that excel at metabolizing heterogeneous waste streams, stabilizing microbiomes or sustaining soil functions over long timeframes may perform modestly when judged only by liters of product per hour and cost per kilogram, and so they tend to be sidelined even when their most significant contributions are systemic rather than linked to a single molecule.

Outside controlled bioreactors, microbes rarely behave as single-product factories at all. They assemble into multispecies communities and microbiomes in which functions are distributed across taxa and constantly reshaped by interactions. Properties emerge at the community level: taxonomic and functional diversity, metabolic exchanges, niche complementarity across space and time, feedbacks with the surrounding environment and redundancy that sustains function under disturbance. These features allow microbial communities to decompose complex organic matter, cycle nutrients, modulate host physiology and coordinate interactions across trophic levels, thereby supporting the regeneration of soils, waters and biogeochemical cycles. The authors connect this to the etymology of regenerare, to create again, a concept in which life creates conditions conducive to life. Long-term regenerative agriculture, for example, has been shown to improve soil organic carbon, enhance nutrient availability and restructure bacterial communities in ways associated with healthier, more resilient soils, benefits that escape short-term yield metrics yet are crucial for sustained productivity and climate adaptation.

Some of the most compelling evidence comes from infrastructure that already runs at industrial scale. Activated sludge, the workhorse of municipal and industrial wastewater treatment, relies on complex microbial communities that remove organic load, transform nitrogen and phosphorus and attenuate pollutants. Metagenome-based analyses of full-scale plants indicate that the activated sludge microbiome is structured into distinct functional niches, with different taxa contributing to carbon degradation, nutrient transformations and the handling of metals and other stressors. Even more striking are full-scale transplantation experiments showing that these communities are remarkably resilient: exchanging the entire sludge between donor and recipient wastewater treatment plants maintained effluent quality, while the recipient microbiome gradually re-established a site-specific community structure over successive solid-retention times. Effluent quality persisted even as the community reorganized itself, a textbook demonstration of emergent, regenerative value that no single strain metric could capture.

Resource-oriented configurations built around purple non-sulfur bacteria extend this logic further. Photoanaerobic treatment of wastewater using these phototrophic microbes has been proposed as an alternative to conventional aerobic polishing, coupling organic load removal with microbial biomass accumulation. High-rate photobioreactors fed with agro-industrial or domestic effluents can recover nitrogen and energy while producing biomass, and mixed-culture systems have been evaluated for single-cell protein and polyhydroxyalkanoate production from domestic wastewater, linking treatment to feed and bioplastic precursors. Nutritional tuning of purple non-sulfur bacteria grown on hydrogen and carbon dioxide further illustrates how such platforms can supply tailored protein profiles for food and feed applications. The primary value of these configurations lies in maintaining water quality and protecting receiving environments over the long term while simultaneously recovering resources, yet this regenerative service is rarely described in terms of industrial relevance, even though it underpins the viability of multiple sectors.

Crucially, the tools needed to measure this hidden value already exist. Multi-omics and systems biology have transformed the capacity to analyze and engineer microorganisms, and in industrial contexts they are widely applied to identify bottlenecks, re-route flux and discover novel enzymes. The same toolbox can be directed toward different questions. Combined metagenomic and metabolomic studies of microbial consortia reveal how community composition, functional redundancy and cross-feeding contribute to robustness in waste degradation and nutrient recovery, characterizing resilience, adaptability and cooperative behavior that sustain function under real-world variability. Work on purple non-sulfur bacteria shows how integrated electrochemical, transcriptomic and metabolomic analyses of biofilms in microbial photo-electrosynthesis reveal how metabolic states can be tuned along redox and resource gradients, while nutrient-limited cultures grown on hydrogen and carbon dioxide show that nitrogen fixation and carbon allocation can be adjusted to modulate the nutritional profile of microbial biomass. Omics can thus function not only to intensify production but as a measurement system for ecological integration, regenerative capacity and network robustness.

To make this reframing operational, the authors propose expanding the notion of industrial relevance along three complementary dimensions. Regenerative capacity describes the ability to restore or maintain key functions in degraded or stressed systems, such as rebuilding soil organic matter, improving water quality or re-establishing balanced microbiomes. System integration measures the degree to which a microbial process can be embedded into existing material and energy flows, contributing to closed-loop configurations rather than operating as a stand-alone production island. Ecological robustness and modularity capture the presence of functional redundancy, syntrophic interactions and flexible network structures that maintain performance under variable inputs and conditions, reducing vulnerability to perturbations. These dimensions do not replace yield, productivity or economic feasibility, but they change what counts as a high-potential microbial platform, elevating non-model organisms and consortia that perform well in waste valorization, ecosystem repair or long-term soil health from niches labeled environmental to the center of industrial strategy.

The stakes extend well beyond microbiology. Recent analyses of nutrient management frameworks and circular economy initiatives for agri-food systems argue that circularity must be integrated with efficiency and sufficiency, yet they show that operational indicators still lean heavily on biomass utilization, productivity and gross domestic product. Studies of actors’ choices regarding nutrient recovery adoption in the food-processing industry and of nitrogen recovery supply chains reach similar conclusions: techno-economic metrics dominate decision-making even when circularity is a stated goal. This misalignment between rhetoric and metrics helps explain why many circular bioeconomy initiatives translate into feedstock shifts rather than structural redesign, risking a biological extension of the fossil paradigm with new inputs but similar logic. Recognizing regenerative functions as part of industrial value requires adjustments at three levels: conceptual, moving beyond the factory metaphor; methodological, using omics and modeling to characterize regenerative traits; and evaluative, integrating regenerative capacity, system integration and robustness into decision frameworks. In this reframing, the bioeconomy is not defined solely by replacing petroleum with cellular feedstocks, but by organizing production and regeneration around the ecological intelligence of microorganisms. Industrial microbiology guided by this perspective can contribute not only new molecules but restored systems, shifting the focus from what microbes manufacture to what they help keep alive.

Subject of Research: Microbiology

Article Title: Are we misreading microbial value? Reframing industrial relevance through regenerative systems thinking

Article References: Zwiercheczewski, P., & Siqueira, J. (2026). Are we misreading microbial value? Reframing industrial relevance through regenerative systems thinking. Frontiers in Industrial Microbiology, 3, Article 1757656. https://doi.org/10.3389/finmi.2025.1757656

Image Credits: AI Generated

DOI: 10.3389/finmi.2025.1757656

Keywords: industrial microbiology, microbial communities, circular bioeconomy, regenerative systems, purple non-sulfur bacteria, omics, misreading, microbial, value, Reframing, industrial, relevance

The research, led by Mauranne Lievens of the Department of Oral & Maxillofacial Surgery at Ghent University Hospital, together with Wim Van Paepegem, Tom Goffin, Geert Villeirs and Renaat Coopman, addresses a long-standing gap in the validation of hospital 3D printing programs. Earlier validation studies typically relied on geometric calibration artifacts, standardized test shapes, or simplified phantom objects—objects that behave well on a build plate but share little with the irregular, thin-walled, undercut-rich anatomy of a human skull. The Ghent team instead designed the study around highly accurate anatomical models, deliberately mimicking the actual workflow a hospital follows when a surgeon requests a patient-specific replica for preoperative planning, implant shaping, or patient counseling.

Accuracy in this context is not a single number but a composite of two distinct properties. The first is trueness, which measures how closely a printed model matches the digital reference design—the deviation between the physical object and the source file that clinicians use. The second is precision, which captures reproducibility: how consistent a printer is when producing the same model repeatedly within a single build (intra-build variability), and how consistent it is across separate print jobs run at different times (inter-build variability). Both dimensions matter clinically. A printer that faithfully reproduces anatomy once but drifts between builds is as problematic as one that is consistently wrong. The team quantified both using root mean square (RMS) error, a standard metric that aggregates surface deviation across the entire model rather than relying on a handful of caliper measurements at convenient points.

The statistical design of the study is one of its most notable contributions. Because the dataset comprised repeated prints of multiple anatomical models at multiple timepoints, the observations were inherently clustered—prints from the same build and models from the same printer are not independent of one another. Conventional statistical tests that ignore this structure can overstate significance. To handle the complexity, the researchers employed a Linear Mixed Model (LMM), a framework widely used in agriculture, ecology and clinical trials but, according to the authors, never before applied in 3D printing accuracy research. The mixed model allowed the team to evaluate fixed effects—printer type, anatomical model, and comparison type—while correctly accounting for the internal clustering of repeated measurements, yielding a statistically robust picture of where true differences lie.

The headline result is that every technology tested achieved accuracy within clinically acceptable limits, an encouraging finding for hospitals that have invested in lower-cost equipment. But the ranking was clear and statistically significant. Material Jetting, which builds objects by jetting tiny droplets of photopolymer in extremely thin layers, achieved a mean RMS error of 67 micrometers—the finest accuracy in the study. Stereolithography, which cures liquid resin layer by layer with a light source, followed at 109 micrometers. Fused Filament Fabrication, the thermoplastic extrusion process behind most affordable desktop printers, came in at 130 micrometers. In practical terms, these differences span roughly the width of one to two human hairs, yet in surgery that margin can matter: when a model is used to pre-bend titanium plates or rehearse the osteotomy of a complex orbital or mandibular reconstruction, sub-millimeter deviations propagate directly to the operating table.

Just as important as the ranking was what the Linear Mixed Model revealed about the drivers of error. Printer type was a significant fixed effect, confirming that the technology choice itself—not random variation—explains much of the accuracy difference. But anatomical complexity also exerted a significant influence, meaning that the shape being printed matters nearly as much as the machine printing it. Models with delicate facial structures, thin bony walls and deep undercuts stressed each technology differently: resin- and jetting-based photopolymer processes handled fine detail more gracefully, while extrusion-based FFF showed characteristic stair-stepping and filament-path limitations on curved, intricate surfaces. This interaction underscores why calibration artifacts alone cannot predict clinical performance; a flat test coupon tells a hospital little about how a printer will cope with a zygomatic arch or a thin orbital floor.

The study’s framing around the Medical Device Regulation gives it particular weight for European hospitals. MDR 2017/745 classifies many in-house manufactured products as devices and requires health institutions to demonstrate that their production, under the responsibility of a single legal manufacturer, meets safety and performance expectations—including verification of accuracy appropriate to the intended clinical use. Point-of-care 3D printing has often grown organically inside surgical departments, with validation practices varying wildly from one hospital to the next. By defining accuracy through trueness and precision, quantifying it with RMS error against digital references, and analyzing it with a defensible statistical model, the Ghent team has effectively published a template for the kind of documented, reproducible validation that regulators and hospital quality officers can build upon.

For hospital administrators weighing procurement decisions, the results offer a nuanced cost-benefit picture. Material Jetting’s superior fidelity comes with higher machine and material costs, and photopolymer processes require resin handling and post-processing protocols. FFF remains the most affordable and accessible option, and the study confirms its output is still clinically acceptable—suggesting it can retain a role for models where extreme fidelity is not essential, such as patient education or approximate planning. SLA sits between the two, offering strong performance at moderate cost. The finding that all three cleared the clinical acceptability bar means the choice can legitimately be driven by the intended application, budget and workflow, rather than by fear that cheaper technology is automatically unsafe. For applications demanding the highest anatomical fidelity within the MDR framework, however, the evidence now points decisively toward Material Jetting.

The research also carries implications well beyond craniomaxillofacial surgery. In-house and point-of-care 3D printing is expanding rapidly into orthopedics, cardiothoracic surgery, dental applications and surgical training, and virtually every one of those programs faces the same questions: how accurate is our printer, how repeatable is it, and can we prove it? The study’s methodological chain—realistic anatomical models, digitized reference comparisons, RMS-based quantification of trueness and precision, and mixed-model statistics that respect the structure of repeated manufacturing data—offers a portable blueprint for any institution seeking to validate its own fleet. The authors note it is the first application of a Linear Mixed Model in this field, and if the approach is adopted broadly, it could standardize how hospitals across jurisdictions demonstrate regulatory compliance. Published open access in the journal 3D Printing in Medicine, with no specific external funding and no declared competing interests, the study was reviewed with the involvement of the Ghent University Hospital Medical Ethics Committee, receiving official approval on July 2, 2024, and received on 16 September 2025, accepted on 16 August 2026, and published on 11 September 2026.

Subject of Research: Comparative accuracy of FFF, SLA and Material Jetting 3D printing for in-house anatomical models in craniomaxillofacial surgery

Article Title: Clinical relevance of accuracy in in-house 3D printing in craniomaxillofacial surgery: a comparative study of FFF, SLA, and MJ technologies

Article References: Lievens, M., Van Paepegem, W., Goffin, T., Villeirs, G., & Coopman, R. (2026). Clinical relevance of accuracy in in-house 3D printing in craniomaxillofacial surgery: a comparative study of FFF, SLA, and MJ technologies. 3D Printing in Medicine. https://doi.org/10.1186/s41205-026-00344-8

Image Credits: AI Generated

DOI: 10.1186/s41205-026-00344-8

Keywords: 3D printing, craniomaxillofacial surgery, anatomical models, FFF, SLA, Material Jetting, Medical Device Regulation, accuracy validation, Linear Mixed Model, point-of-care manufacturing, Clinical, relevance