Lignocellulosic biomass conversion is still limited by pretreatment strategies that are energy-intensive, chemically harsh, and difficult to scale. In contrast, termites and their symbiotic microbiota represent one of the most efficient natural systems for lignocellulose deconstruction, achieving rapid biomass conversion under mild physicochemical conditions. This review synthesizes current knowledge on termite-bacteria symbiosis and interprets it from a bioprocess engineering perspective, positioning it as a model for the design of engineered lignocellulosic biorefineries. By integrating gut compartmentalization, microbial community organization, enzymatic repertoires, and interaction networks, we develop a systems-level view of how spatial organization, physicochemical gradients, and cooperative metabolism jointly enable efficient biomass processing. Comparative analysis with conventional microbial platforms and industrial pretreatment technologies highlights the process advantages of termite-inspired systems in energy demand, process integration, and scalability. Rather than providing a purely descriptive synthesis, this review translates biological organization into engineering logic, outlining practical design principles for reactor architecture, consortium assembly, and enzyme deployment. We further identify key bottlenecks that constrain industrial translation, including transport limitations, gradient control, and community stability. Together, these insights provide a structured foundation for the rational development of robust, scalable, and sustainable termite-inspired lignocellulosic bioprocesses.

Biomanufacturing is increasingly transitioning toward sustainable non-grain carbon feedstocks, including gaseous C1 compounds, liquid C1 substrates, and solid non-grain biomass. However, the efficient utilization of these feedstocks remains hindered by mass-transfer limitations, thermodynamic constraints, substrate and intermediate cytotoxicity, and the inherent physical limitations of macromolecular catalysts. Intracellular multienzyme cascade catalysis offers a promising solution by organizing highly coordinated catalytic networks within living cells, thereby enhancing metabolic efficiency and overcoming multiple bioconversion barriers. In this review, we systematically analyze the mechanisms and engineering strategies underlying whole-cell cascade systems from the integrated perspectives of material, energy, and information flows. Based on these principles, we propose a universal diagnostic and design framework for the bioconversion of non-grain carbon feedstocks. We further illustrate how these general design rules can be tailored and implemented at the systems level for the efficient conversion of specific substrate classes. In addition, we highlight the emerging role of artificial intelligence in enabling predictive system reconstruction through generative design, global pathway optimization, and automated design-build-test-learn (DBTL) cycles. Collectively, this review establishes a unified theoretical and practical framework for the rational design of next-generation biomanufacturing platforms based on intracellular multienzyme cascade catalysis.

Live bacterial vectors combine intrinsic adjuvanticity, mucosal targeting, and programmable antigen delivery within a single engineerable chassis, yet their development has historically relied on empirical attenuation rather than systematic design. We present a Design-Build-Control framework, adapted from synthetic biology paradigms, that reframes bacterial vector engineering as a unified biotechnological problem. Design principles govern trade-offs among safety, immunogenicity, tissue tropism, and route of administration. Build-stage strategies translate these principles into genetically stable vector platforms through attenuation, balanced-lethal systems, and antibiotic-free plasmid maintenance. Control-stage tools, including inducible promoters, quorum-sensing circuits, and kill switches, enable dynamic regulation of vector behavior within the host. We apply this framework across major platform classes, including live attenuated Gram-negative and Gram-positive bacteria, probiotic chassis, and non-replicating systems such as outer membrane vesicles, bacterial ghosts, and spores, and examine how biomaterial-based formulation, including encapsulation and mucoadhesive coatings, extends engineering control into manufacturing and delivery. We evaluate translational determinants, including genetic stability, environmental containment, and regulatory compliance, alongside applications spanning infectious disease, oncology, and veterinary medicine. We further assess the emerging role of artificial intelligence in bacterial-vector design, distinguishing established computational applications, experimentally validated approaches, and prospective uses that still require experimental validation. By integrating advances in synthetic biology, biomaterials, and computational design, this review presents programmable bacterial vectors as an emerging biotechnology platform and proposes a Design-Build-Control (DBC) framework for systematically evaluating biosafety, manufacturability, and clinical translation.

Nannochloropsis has developed from an oleaginous marine microalga valued mainly for eicosapentaenoic acid (EPA)-rich lipids into a credible candidate for marine photosynthetic biomanufacturing. Its toolkit now spans genome resources, efficient nuclear transformation, homologous recombination, CRISPR/Cas editing, marker recycling, defined integration sites, and modular multigene assembly. Researchers have used these capabilities to alter carbon allocation, triacylglycerol accumulation, EPA synthesis and lipid-class partitioning, carotenoid metabolism, recombinant-protein production, specialty-metabolite formation, and cultivation robustness. This review assesses whether these advances are sufficient to support Nannochloropsis as a predictable, reusable, and process-aware chassis. The evidence shows that the genus has moved beyond basic transformability. Targeted genome modification and pathway construction are feasible, and native and non-native products have reached biologically relevant and, in some cases, process-relevant levels. However, outcomes remain strongly influenced by genomic locus, strain background, intracellular compartment, physiological state, and cultivation regime. Platform maturation will therefore depend on reusable genomic architectures, reliable organelle-targeting rules, dynamic regulation, and phenotyping suited to iterative design-build-test-learn workflows. Strain design must also be coupled more closely to cultivation, harvesting, product recovery, and deployment. Product-specific chassis configurations are likely to be more practical than a universal strain for EPA-rich oils, pigments, recombinant products, and specialty metabolites. Nannochloropsis thus provides a useful test of how an industrially relevant, non-model photosynthetic eukaryote can become a more designable marine production host.

Alternative splicing (AS) generates transcript and protein isoforms, but its evolutionary significance cannot be inferred from isoform number alone. This review uses a comparative framework based on recent literature to separate splice-variant origin, functional consequence, and evolutionary retention in plants and animals. We synthesize evidence from long-read sequencing, single-cell isoform profiling, pangenomics, proteogenomics, and perturbation studies. Animal studies emphasize exon-level switches in neural, cardiac, immune, and germline programs, whereas plant studies highlight intron retention, exitron splicing, environmental responses, and polyploidy-associated regulation. These are tendencies shaped by biological and sampling differences, not exclusive properties of either kingdom. Three worked examples illustrate how reproducible regulation, conservation or population data, molecular consequences, and targeted perturbation support different levels of inference. Functional effects alone do not establish historical adaptation, and conservation alone does not identify the trait under selection. The framework helps prioritize isoforms for crop improvement, biomarkers, and RNA intervention. Clinical splice modulation and field-evaluated natural rice alleles demonstrate translational potential, while highlighting the distinction between validated outcomes and prospective engineering. Future studies should connect isoform-resolved measurements to causal perturbation and fitness or agronomic performance across relevant environments.

Conventional aptamer discovery by systematic evolution of ligands by exponential enrichment (SELEX) is often constrained by limited library capacity, PCR bias, and multi-round screening. The recent convergence of artificial intelligence with aptamer development is driving a paradigm shift from empirical screening toward rational, data-driven design. This Review presents a timely and comprehensive overview of AI integration across the entire aptamer development pipeline, with a particular emphasis on advances reported in 2024-2026. We begin by discussing bioinformatics databases and computational tools for high-throughput SELEX data analysis. We then review state-of-the-art strategies for secondary and tertiary structure prediction, including recent breakthroughs such as AlphaFold 3, RoseTTAFoldNA, and diffusion-based approaches. We also cover machine learning methods for aptamer-target interaction prediction, molecular docking, dynamics simulations, and free energy calculations. A major focus is placed on very recent AI-driven generative design, including diffusion models (AptaDiff), language model-based frameworks (InstructNA, GRAPE-LM), and reinforcement learning approaches (AiDTA) for de novo aptamer generation and optimization. Emerging applications of AI-designed aptamers in precision diagnostics, targeted therapy, and biosensing are also surveyed. Finally, we discuss current challenges and outline future directions toward closed-loop, interpretable AI frameworks for translational aptamer development. By synthesizing the latest computational and experimental advances, this Review aims to serve as a timely resource for researchers seeking to leverage AI for next-generation aptamer discovery.

Plant base editing enables targeted nucleotide substitutions and provides a powerful approach for functional genomics and crop improvement. The toolkit has expanded from canonical cytosine and adenine editors to dual-base, transversion, Cas12-based, and organelle-targeted systems, broadening the range of accessible mutations and genomic targets. These advances create new opportunities for generating useful alleles, modifying gene function, and enabling directed evolution. However, expanded base-editing capabilities do not necessarily ensure predictable outcomes or successful breeding applications. This review summarizes the development of plant base editors, computational tools for target design, and their applications in crop research. We examine editor architectures and compare editing efficiency, product purity, and specificity, with particular attention to transversion editing. We also highlight the intrinsic technical limitations of base editors and the bottlenecks encountered when moving from laboratory research to field applications. By connecting base editor development with practical constraints, this review provides guidance for editor selection and identifies priorities for applying base editing to crop improvement.

Microorganisms, endowed with extraordinary metabolic versatility and environmental adaptability, represent a transformative frontier for addressing global sustainability imperatives. Engineering microbial systems through regulatory network rewiring and synthetic biology-driven functional augmentation substantially amplifies their biotechnological utility. Herein, we review the convergence of synthetic biology, bio-electrocatalysis, and reactor engineering as an integrated framework for sustainable biohydrogen production. Comparative analysis with conventional hydrogen generation strategies underscores the superiority of bioelectrochemical systems (BESs), which simultaneously enable wastewater valorisation and clean hydrogen production through microbially driven electron transfer cascades. Moreover, the mechanistic electron generation-to-hydrogen conversion continuum highlights the indispensable dominance of biotic catalysis. Furthermore, CRISPR-Cas9-mediated multiplex genome editing is examined as a precision tool surpassing traditional mutagenesis approaches for pathway optimisation. Key electrogenic taxa, their electrochemical interfaces and influences within BESs architectures, prevailing technological bottlenecks in scalability, techno-economic feasibilitites and forward-looking strategies toward wholly biological, scalable hydrogen production systems are critically featured.

Outer membrane vesicles (OMVs) are spherical, nano-sized proteoliposomes naturally secreted by Gram-negative bacteria. By incorporating parental membrane components and encapsulating diverse cellular cargos, OMVs function as potent mediators of biological communication. Despite inherent challenges, including low production yields, heterogeneous composition, and stochastic cargo loading, OMVs hold immense promise across broad biotechnological landscapes. In this review, we examine current advancements in OMV chassis engineering and functional diversification, providing a comprehensive synthetic biology perspective on their role as modular and programmable systems. We discuss five established strategies for inducing hyper-vesiculation alongside two complementary frameworks for constructing synthetic vesicles. These engineered OMVs facilitate transformative applications spanning biomedical and biotechnological domains, notably as high-fidelity platforms for studying membrane proteins within their native lipid environments. As synthetic biology increasingly converges with materials science, future innovations such as stimuli-responsive vesicles, self-assembling architectures, and hybrid bio-synthetic materials are poised to emerge. Such advancements position synthetic OMVs at a critical intersection to address complex challenges in medicine, environmental remediation, and advanced materials science, offering unprecedented opportunities for both fundamental discovery and industrial applications.

The hydroprocessed esters and fatty acids (HEFA) pathway is a technically mature and commercially deployable process for producing sustainable aviation fuel (SAF) from lipid feedstocks. Nevertheless, the current supply of sustainable lipids is insufficient to meet the projected demand for SAF, emphasizing the need to identify alternative lipid sources. This review comprehensively evaluates the potential of insects as biological conversion platforms for transforming low-lipid organic wastes into lipid-rich biomass suitable for HEFA-based SAF production. The lipid contents and compositions of three representative insect species (black soldier fly larvae (BSFL), housefly larvae (HFL), and mealworms (MW)) are compiled and compared. The assessment indicates that fatty acid saturation and chain-length distributions are key determinants of the suitability of insect lipids for HEFA processing, influencing hydrogen demand and upgrading requirements. Among these species, BSFL-derived lipids show potential because of their high proportion of saturated fatty acids, especially lauric acid, which may reduce hydrogenation requirements and provide hydrocarbon chain lengths close to the jet-fuel range. Integration of insect-mediated waste conversion with existing HEFA infrastructure offers a potential route to expand the supply of waste-derived lipid feedstocks. However, the integrated waste-to-insect lipid-to-SAF pathway has not yet been demonstrated, and its environmental benefits and commercial feasibility require whole-pathway evaluation. Consequently, this review proposes insect-mediated lipid production as a complementary feedstock strategy for HEFA-based SAF and identifies feedstock consistency, contaminant control, experimental validation of catalytic upgrading and fuel properties, and integrated life-cycle and techno-economic assessments as priorities for practical implementation.

Tryptophan-drived neuroactive compounds, including serotonin, melatonin, 5-hydroxytryptophan (5-HTP), psilocybin, and related indole derivatives, represent an important class of therapeutics for depression and other neuropsychiatric disorders. However, conventional production through chemical synthesis or natural extraction remains limited by low sustainability, poor selectivity, and high manufacturing costs. Advances in synthetic biology, metabolic engineering, and enzyme engineering have enabled the development of microbial cell factories for the efficient biosynthesis of these high-value compounds. This Review highlights recent progress in engineering microbial platforms for antidepressant tryptophan derivatives, focusing on tryptophan overproduction, hydroxylation biocatalysts, downstream biosynthetic pathways, and cofactor engineering. We further discuss the emerging roles of bacterial heme-dependent tryptophan hydroxylases and AI-assisted enzyme and pathway engineering in expanding the biosynthetic toolbox. Finally, we outline current challenges and future opportunities for integrating intelligent strain design with advanced bioprocess engineering to advance the biomanufacturing of tryptophan-derived neuroactives.

Directed evolution (DE) represents a pivotal interdisciplinary paradigm that synergistically integrates biotechnology, computational methodologies, and precision analytical instrumentation. It enables accelerated, controllable in vitro laboratory evolution of diverse biomolecules toward predefined functional targets. This review comprehensively summarizes fundamental principles and cutting-edge progress underlying core DE frameworks, encompassing mutagenesis engineering, biosensing, high-throughput screening (HTS), cell-free and display techniques, and integrated computational approaches. We systematically elaborate on their intrinsic features, comparative merits, inherent bottlenecks, applicable scopes, and prospective optimization trajectories. Furthermore, we emphasize the transformative capacity of these technologies for the development of organisms, enzymes, antibodies, and diverse functional biomolecules with tailored functional properties, spanning industrial, clinical, and fundamental research applications. Collectively, this work aims to provide a holistic mechanistic framework of contemporary DE platforms and inspire novel conceptual and methodological directions for future research.

Microalgal strains are engineered and benchmarked under fixed conditions, yet industrial cultures experience light, temperature, CO2, salinity and nutrient regimes that shift from seconds to seasons. Programmable microalgae, cells that sense environmental change, integrate signals and adjust their physiology (Sense-Decide-Adapt, SDA), could close this gap. Native nutrient-sensing, photoprotective, carbon-concentrating and osmotic programs already approximate SDA architectures, and genome editing, biosensors and single-cell screening make them tractable to rewire, although direct evidence that altering response dynamics raises productivity remains sparse. Here, we introduce the Environmental Performance Envelope (EPE), a framework defining the region of environmental space in which a strain retains a defined fraction of its reference productivity under realistic trajectories. The EPE turns a design question into an experiment: does engineering expand the operating space or merely relocate the optimum? A staged roadmap, including CO2-fluctuation trials and evolutionary stability testing, shifts engineering targets from static traits to response kinetics, recovery, heterogeneity and stability, and couples cellular control to bioprocess design, pointing toward more robust algal biomanufacturing. Because environmental variability constrains any living system deployed outside the laboratory, the same logic may extend to other microbial cell factories, crops and engineered organisms.

Fungal-microbial interactions drive key ecosystem functions and offer vast, untapped potential for the bio-economy. However, translating these natural consortia into controllable applications is hindered by the structural and metabolic complexity of the interplay. This review navigates the complexity landscape of these interactions, examining the technological trajectory from the mechanistic foundation of 1:1 dialogue to the rational design of synthetic consortia. We discuss the transition from static cultivation toward advanced microfluidic ("fungi-on-a-chip") and biomimetic platforms that resolve the spatial ecology across diverse environmental gradients. Beyond extracellular associations, we highlight the paradigm shift toward synthetic endosymbiosis, where precise interaction engineering enables the creation of novel endosymbiotic chassis. Furthermore, we examine how integrating multi-omics with artificial intelligence-driven phenotyping and genome-scale metabolic modeling can bridge the gap between mechanistic discovery and predictive design through integrative systems biology and design-build-test-learn cycles, enabling the rational engineering of fungal-microbial consortia. These approaches offer opportunities to iteratively optimize community composition, metabolic exchange, and functional performance, thereby connecting laboratory-scale discovery with ecosystem-level complexity. Despite these advances, further development and integration of these approaches are needed to improve the reproducibility, scalability, and controllability of fungal-microbial consortia for agriculture, environmental, and industrial applications.

Anaerobic digestion (AD) is increasingly recognized as a foundational process in the global shift toward sustainable, circular, and low-carbon systems. Long established for its capacity to convert organic waste into biogas and nutrient-rich digestate, AD now plays a pivotal role in climate-smart production, decentralized energy generation, and resource recovery. Yet its full potential extends far beyond these traditional outputs. Advances in microbial ecology and process engineering are transforming AD into a modular biomanufacturing platform capable of producing high-value compounds. These biomolecules offer renewable pathways for the synthesis of biofuels, single-cell protein (SCP), methanol, microalgal products, and other speciality chemicals, positioning AD at the heart of emerging bioeconomies. This dual capacity, addressing today's urgent need for sustainability and enabling tomorrow's bio-based production system, positions AD as an important technology in future biorefineries. This article appeals to a broad readership seeking to understand AD, which is one of the most mature yet dynamically evolving processes for biological resource recovery. Increasingly positioned as a central platform for future circular bioeconomies and sustainable biomanufacturing, this review specifically focuses on pivotal aspects of next-generation biorefineries, including biomethanation, SCP and methanol production, microalgal-based products, and integrated resource recovery strategies.