Red-cell therapeutics are often discussed as a single class, but current products use erythroid biology in distinct ways. Intact red blood cells (RBCs) are long-lived, deformable cells, circulating for about 120 days in humans and roughly 40-60 days in mice, and repeatedly contact immune, endothelial, and reticuloendothelial cells. They are best suited to applications that benefit from intravascular persistence and repeated surface interactions, including enzyme carriage, durable ligand or antigen display, immune modulation, and hitchhiking designs. RBC-derived extracellular vesicles (RBC-EVs) share erythroid membrane lineage at a smaller scale but operate by a different logic. Rather than relying on prolonged circulation, RBC-EVs derive their value from hours-scale routing to recipient cells, cargo protection, uptake, endolysosomal release, antigen processing, and functional intracellular cargo activity after entry. This review argues that red-cell therapeutics are best understood by engineering strategy rather than disease indication alone. For intact RBCs, the central task is to add therapeutic function while preserving deformability, membrane organization, antigen integrity, immune compatibility, and acceptable clearance. Major strategies include covalent conjugation, affinity anchoring, lipid insertion, enzymatic ligation, genetic or precursor-cell engineering, and hitchhiking-based surface association, each balancing functional gain against biological burden. For RBC-EVs, the design problem shifts to vesicle generation, cargo loading and retention, surface functionalization, targeting, intracellular delivery, storage stability, potency, and batch reproducibility. Future progress will require engineering routes that increase efficacy while reducing perturbation and manufacturing complexity, supported by scalable erythroid sources, standardized downstream processing, and release criteria matched to each platform.
Precision fermentation has emerged as a promising platform for producing food ingredients. However, only a limited number of precision fermentation-derived ingredients have reached commercial markets. The concept of the minimum viable product (MVP) provides a useful framework for accelerating the practical adoption. MVP in precision fermentation is a food ingredient that provides a clear value proposition for a defined target consumer and can be safely and economically produced by an engineered microorganism. We introduce the Precision Fermentation Ingredient Pyramid, which can categorize targets into three tiers based on production volume, market value, and functional roles. A pipeline integrates market needs, biological and regulatory feasibility, economic competitiveness, and scalability for MVP selection. Applying this framework, we highlight opportunities across ingredient classes, including single-cell protein, ovalbumin, and β-carotene. Finally, we highlight the importance of techno-economic analysis for precision fermentation, particularly in linking strain performance and downstream processing to process economics.
Photosynthetic microorganisms can convert sunlight and CO₂ directly into biomass and bioproducts. Yet, most biofoundries still optimize heterotrophic chassis reliant on agricultural sugars, limiting impact on global decarbonization. This review argues that sustainable manufacturing requires integrating microalgae and cyanobacteria into Design-Build-Test-Learn pipelines and shifting from biomass conversion to light- and CO₂-driven production. We highlight advances in genetic and cellular engineering in model photosynthetic microbes, including modular cloning, genome editing, and organelle engineering that enable pathway design for lipids, isoprenoids, and proteins. We discuss phototroph-specific bottlenecks for automation and standardization, including slower growth, variable transgene expression, chlorophyll autofluorescence, and the need for controlled illumination and gas exchange with linked data pipelines. Finally, we examine cultivation and scale-up constraints, emphasizing co-optimization of strain traits, reactor design, and downstream processing to improve techno-economic and environmental performance. Photosynthetic biofoundries are therefore both necessary and increasingly feasible for a low-carbon bioeconomy.
Organic contaminants from natural and anthropogenic sources threaten global water and food security. While bioremediation offers significant mitigation potential, tracking compound degradation in complex ecosystems remains challenging. Detection technologies span from microscale methods - biosensors, imaging, and volatile organic analysis - to landscape-scale remote sensing. Each technique provides unique information, but integrating these disparate data streams is a major bottleneck hindering ecological-scale tracking and assessment. In this review, we propose a holistic monitoring framework for detecting contaminants and tracking the progress of bioremediation, highlighting minimally invasive detection techniques and cross-scale sensor integration. We discuss emerging technologies and the generation of standardized datasets essential for machine-learning applications in predicting degradation trajectories using fabricated ecosystems. Integrating environmental sensing, microbiome science, and advanced analytics provides a new chassis for interrogating remediation efforts.
Since the emergence of synthetic biology, biofoundries have developed as enabling infrastructures that scale engineering biology globally. Landmark initiatives, such as Genome Project-Write, JCVI-syn3.0, Sc2.0, SynMoss and the Synthetic Human Genome Project, have significantly advanced the feasibility of constructing chromosome-sized DNA and revealed key principles of genome function and design. Nevertheless, the intrinsic complexity of cellular systems and the resource-intensive nature of experimental design-build-test-learn cycles continue to constrain innovation. Recent advances in artificial intelligence (AI), whole-cell modelling and digital twinning are now creating opportunities for self-improving, AI-driven biofoundries that seamlessly integrate in silico design and validation with miniaturised and automated in vitro testing. This review surveys the technologies shaping AI-driven synthetic biology, highlighting their convergence with automation, digitisation and miniaturisation to enable fully autonomous biofoundries that unify computational design, automated fabrication and data-driven learning within a single adaptive framework.
The growing demand for sustainable biomanufacturing has increased interest in nonfood, low-cost carbon feedstocks, particularly methanol and mannitol, owing to their availability and high energy density. However, conventional model microorganisms often show limited efficiency and robustness on such substrates, motivating the exploration of alternative chassis. This review highlights Bacillus methanolicus as an emerging biomanufacturing platform capable of efficiently utilizing both methanol and mannitol. Its thermophilic growth, seawater adaptation, and energy-efficient methanol assimilation pathway provide intrinsic advantages for resource-efficient fermentation processes. We summarize current insights into methanol and mannitol metabolism in B. methanolicus, highlighting that regulation of methanol oxidation and formaldehyde detoxification determines efficient co-utilization, as well as recent advances in synthetic biology tools development that enable rational strain engineering. Finally, we discuss challenges in biomanufacturing from methanol and mannitol, and outline future directions toward establishing B. methanolicus as a versatile platform connecting one-carbon and marine-derived substrates to sustainable chemical synthesis.
Vitamin A and its derivatives (retinoids) are essential for vision, immunity, and cellular differentiation. Microbial biosynthesis offers a sustainable alternative to chemical synthesis for producing retinoids and their precursors. This review summarizes the recent advances in metabolic engineering of microbial cell factories for enhanced production of β‑carotene, retinol, retinal, retinoic acid, and retinyl esters. Key strategies, including strengthening precursor supply, cofactor and transporter engineering, and the identification and engineering of key enzymes such as β-carotene 15,15'-oxygenase, retinol dehydrogenase, retinal dehydrogenase, and retinol acyltransferase, were summarized. Additionally, the cultivation optimization approach addressing the oxidative instability of vitamin A is introduced. Future strategies for advancing metabolic engineering toward industrial-scale production are also discussed.
Medium- and long-chain dicarboxylic acids (M/LCDAs) are key monomers for the synthesis of nylons and high-performance engineering plastics. Compared to traditional chemical methods, microbial synthesis offers advantages such as environmental friendliness and high regioselectivity. However, its industrial application remains limited by bottlenecks, including low mass transfer efficiency on hydrophobic substrates, instability of key oxidase systems, and cellular metabolic imbalances. This review summarizes recent strategies leveraging enzyme engineering, systems metabolic engineering, and diverse synthetic biology approaches to overcome current limitations in the biosynthesis of M/LCDAs. We specifically highlight mechanisms for enhancing the transmembrane transport of hydrophobic substrates and the mining of novel transporters. Furthermore, we elaborate on protein engineering efforts targeting key enzymes (e.g. cytochrome P450s), covering rational design, fusion expression, and novel dimerization techniques. At the systems level, we discuss metabolic network regulation achieved through the construction of the reverse β-oxidation cycle (r-BOX) and the reprogramming of cofactor regeneration and energy metabolism. Finally, future perspectives on integrating AI-aided design and waste valorization are proposed to provide theoretical guidance for the efficient and sustainable biomanufacturing of M/LCDAs.
Advances in next-generation sequencing (NGS) have provided refined insight into bacterial transcriptional regulatory networks (TRNs) on a genome-wide scale. The integration of genomic and transcriptomic datasets has clarified the architecture of TRNs, revealing regulatory complexity that requires computational methods for interpretation. The TRNs and their regulatory elements characterized through these analyses serve as a foundation for systems biology. In this review, we discuss the progression from targeted molecular characterization to systems-level approaches. NGS-based characterization at near-single-base-pair resolution has allowed precise interrogation of DNA-binding protein dynamics. Machine learning-based frameworks, such as independent component analysis, have enabled the identification of co-regulated gene modules and the discovery of novel regulatory relationships within transcriptomic datasets. In addition, deep learning models have shown utility in uncovering transcriptional regulatory elements and guiding the de novo design of regulatory sequences. Together, these tools are being integrated into closed-loop biofoundry platforms, accelerating automated workflows in bacterial systems engineering.
The Genesis Mission is a U.S. initiative to accelerate bioproduction by integrating synthetic biology with the artificial intelligence (AI) ecosystem. However, it also raises caution regarding AI-driven biotechnology. Biomanufacturing requires the coordinated optimization of microbial metabolism and large-scale bioreactor operations. Machine learning (ML), automation, and large language models (LLMs) can streamline integration of literature and real-time data for multiscale optimization and "digital twin" development. But uncertainty in scale-up performance and commercial risk continue to challenge microbial factory deployment because strains optimized through laboratory design-build-test-learn cycles often underperform in stressed industrial bioreactors. Addressing these gaps will require thorough investigation of strain performance under industrial bioreactor conditions, followed by the development of shared AI-ready biosystems databases, integrative AI methods (e.g., transfer learning, reinforcement learning, and Bayesian Optimization), hybrid digital cell modeling, and technoeconomic analysis across the process chain.
In industrial biomanufacturing, downstream processing (DSP) remains a primary determinant of economic viability and operational complexity. While conventional process optimization focuses on refining reaction units, a transformative paradigm of "upstream design enabling downstream simplification" has emerged. This review systematically evaluates five representative microbial engineering strategies to streamline DSP: secretion pathway engineering, product modification, cell surface engineering, cell morphology engineering, and programmed cell lysis. These upstream interventions decouple product synthesis from complex separation matrices and lower separation costs by promoting extracellular secretion, facilitating intracellular product release, or enabling targeted purification. By systematically summarizing the principles, applications, and advantages, this review provides a strategic framework for developing next-generation microbial cell factories that harmonize upstream productivity with downstream efficiency, ultimately paving the way for more cost-competitive and sustainable bio-production.
Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.
Lignin represents a promising and sustainable feedstock for the production of high-value products. However, its heterogeneity and recalcitrance pose a big challenge for efficient depolymerization and upcycling. This review highlights recent advances in microbial lignin valorization, focusing on three key steps: lignin depolymerization, metabolism of lignin-derived aromatic compounds, and valorization to target products. Recent progress in lignin depolymerization is enabling the discovery and optimization of more efficient and broadly specific ligninolytic enzymes, and highlights the critical role of auxiliary enzymes and quinone redox cycling in supporting ligninolytic activity. New catabolic mechanisms, transport systems, and transcriptional regulation networks for both dimeric and monomeric lignin-derived substrates expand our understanding of biological funneling pathways, and they offer valuable tools for designing more efficient microbial biocatalysts and biosensors. Emerging metabolic engineering and adaptive laboratory evolution strategies for creating robust microbial chassis capable of producing diverse value-added products from lignin-derived feedstocks are discussed.
This review summarizes recent advances in the metabolic engineering of microorganisms for the valorization of C2 feedstocks into high-value chemicals and materials. We first discuss native and engineered C2 assimilation pathways, including reverse β-oxidation, aldol-condensation-based carbon extension, and thiamine pyrophosphate-dependent modules, highlighting representative strain designs in Escherichia coli, Pseudomonas spp., and photosynthetic hosts. We then examine C1-to-C2 platform strategies that couple acetogens or gas-fermenting microbes with C2-assimilating production strains. Finally, we outline the rapidly growing toolbox of non-natural and computationally designed pathways that rewire carbon flux with minimal loss. Particular emphasis is placed on the integration of synthetic biology, enzyme engineering, genome-scale metabolic models, and artificial intelligence-driven design for building next-generation, electrified, and digitally guided C2 biorefineries. These advances are positioning C2-based biomanufacturing as a key pillar of low-carbon chemical production.
Host-induced gene silencing (HIGS) is a crop protection strategy that exploits RNA interference (RNAi) to silence targeted genes in invading pathogens or pests and reduce disease. Despite some successful examples of HIGS in laboratory settings, its translation into commercial agriculture has been limited. Recent discoveries demonstrating that plants deploy specific endogenous small RNAs (sRNAs) to regulate gene expression in fungi and oomycetes have broadened our understanding of natural trans-species RNAi (natural-tsRNAi) and provided a framework for improving applications of sRNA-based defense. In this review, we summarize HIGS studies published between 2021 and 2025 with a meta-analysis, highlighting their potential and limitations. We then discuss recent advances in natural tsRNAi with an emphasis on the secondary small interfering RNA pathway as a native immune response. Finally, we provide our opinion on how insights from natural tsRNAi inform design principles for sRNA-based immunity as a promising source of engineering durable resistance traits.
Sustainable biomanufacturing requires moving beyond fossil and agricultural carbon sources. To date, industrial biotechnology depends largely on starch, sugar, and plant-derived glycerol, creating competition with food and feed production and pressure on limited arable land. Yet, microbial metabolism is not inherently constrained to these substrates, opening opportunities for alternative feedstocks. Non-agricultural carbon sources, particularly single- and two-carbon (C1 and C2) compounds, offer a compelling alternative. Produced from CO2 via electrochemical or biological routes, methanol, formate, acetate, and ethanol connect renewable energy with microbial synthesis while enabling carbon recycling. Their use, however, introduces distinct metabolic and thermodynamic constraints, including limitations in energy conservation, redox balance, and pathway driving forces. Here, we examine C1 and C2 assimilation pathways in yeasts, highlighting key bottlenecks and engineering advances that make sustainable circular biomanufacturing possible.
While global demand for lanthanides (Ln) is projected to rise sharply over the next decade, geographically concentrated supply chains that are sensitive to disruption create a major bottleneck for meeting future needs. Secondary feedstocks offer a potential alternative, but their low Ln concentrations and matrix complexity limit the effectiveness of conventional hydro- and pyro-metallurgical separation and enrichment. Biological systems offer selective, low-energy alternatives to conventional Ln recovery methods. Engineered Ln-binding proteins now achieve affinities and intra-Ln selectivities that, in purified form, rival or exceed those of synthetic chelators. Yet whole-system recovery depends not only on binding performance but also on envelope permeability, transport kinetics, accumulation, release, and stability under industrial leachate conditions. Moreover, no engineered microbial chassis to date integrates recognition, intracellular trafficking, accumulation, and controlled release into an end-to-end separation pipeline. Here, we outline how chassis selection and new biodesign strategies can facilitate the move from bioleaching to a full recovery pathway. This requires integrating Artificial Intellegence / Machine Learning (AI/ML)-guided design, genome engineering tools, high-throughput phenotyping, and biophysical transport modeling within a Design-Build-Test-Learn cycle applied to Ln recognition, trafficking, accumulation, and release.
The pressing challenges of plastic pollution and fossil resource reliance drive the need for sustainable biomanufacturing using non-food feedstocks. Halomonas, an extremophile chassis for next-generation industrial biotechnology, grows rapidly in high-salt, alkaline conditions to suppress contamination, enabling robust open fermentation for bio-based polymers such as polyhydroxyalkanoates and high-value chemicals. This review focuses on two key areas: (1) native and engineered metabolic pathways in Halomonas for diverse carbon substrate assimilation; and (2) multi-layered engineering strategies for enhancing substrate utilization and stress tolerance, including metabolic flux redirection and evolutionary engineering approaches. Furthermore, we outline future directions for advancing Halomonas as an industrial chassis, such as improving its tolerance to inhibitors present in non-food feedstocks, expanding the genetic and regulatory toolkits for precise strain engineering, and diversifying its product portfolio. Collectively, these advances are critical for establishing Halomonas as a versatile and robust microbial platform for a circular bioeconomy.
Multiplex genome editing (MGE) enables coordinated modification of multiple genomic loci and is foundational for engineering complex biological traits. Traditional CRISPR-Cas nuclease-based strategies rely on DNA double-strand breaks (DSBs), which limit precision and pose scaling challenges for incorporating simultaneous edits across different loci. Recent advances in genome editing technologies that operate without generating DSBs have expanded the accuracy and feasibility of multiplexed genomic manipulation. This review focuses on emerging strategies for precise MGE, including base editing, prime editing, and related genome rewriting platforms. We highlight key engineering principles that impact the success of scalable multiplexing, including the choice of editing platform, edit size, and guide RNA architecture, and discuss applications across mammalian, plant, fungal, and bacterial systems. Together, these technologies establish MGE as a versatile framework for precise multigene control in biotechnology and agriculture.
The Design-Build-Test-Learn (DBTL) cycle forms the basis of modern strain engineering and has accelerated the development of microbial cell factories through increasing automation. Despite significant advances in design and build capabilities, physiology-aware testing and predictive learning remain limited. High-throughput screenings often generate large but shallow datasets that cannot identify mechanistic bottlenecks or ensure robustness under industrial conditions. Furthermore, strain engineering and bioprocess development are frequently treated as sequential rather than integrated activities, leading to scale-up failures and costly late-stage corrections. We propose extending the DBTL framework by treating cell physiology and process constraints as key design variables and integrating automated strain construction, production-relevant phenotyping, and computational models linking genotype, phenotype, and process parameters.