Synthetic Biology

Protein synthesis using recombinant elements (PURE) system has been widely applied in various biological research fields and synthetic cell construction. Optimization efforts to enhance the PURE system performance by adjusting its individual components have remained limited to the expression of single genes with a small number of molecular compositions tested, making it difficult to link component composition to system-level performance across different DNA contexts. Here, we combine automated acoustic liquid handling with an active learning framework to explore broadly the compositional landscape of PURE system. By grouping the 69 individual components (including proteins and tRNAs) into 21 functional sets and iteratively guiding experiments with active learning, we rapidly identify improved compositions and demonstrated up to 3-fold enhancement in protein yield and translation rate for a single reporter gene. We further show that optimization drivers differ between low and high DNA concentrations, revealing that optimal PURE compositions are DNA concentration-dependent. We then apply this optimization strategy to enhance the expression of a 41-kb synthetic chromosome containing 15 genes by maximizing the fluorescence intensities of two reporter proteins. While a 3-fold improvement could be reached on the two gene products guiding learning, a full proteomic analysis revealed that optimization is gene-specific, i.e., changes in PURE system compositions differently impact the amounts of synthesized proteins encoded on the same DNA template. Together, this work establishes active learning as an efficient strategy to navigate the high-dimensional PURE compositional space and provides mechanistic insight into DNA context-dependence of gene expression optimization.

Course-based Undergraduate Research Experiences (CUREs) have emerged as a transformative approach to science education, expanding access to authentic research opportunities beyond the traditional undergraduate research assistant (URA) training. By embedding research into a curriculum, CUREs engage a broad and diverse population of students in a classroom environment that emphasizes experimental design, data analysis, and scientific communication. However, this has been difficult to develop for fields such as plant synthetic biology due to the long timescales of plant transformation. One avenue around this problem is to utilize a recent innovation that enables high throughput and rapid screening of gRNA efficacy by leveraging viral-based delivery of guide RNAs (gRNAs). In this work, we develop and validate a CURE with undergraduate students at Colorado State University (CSU). Students worked in teams to design and test efficacy of gRNAs targeting a Cas9-based transcriptional repressor to different regions of the promoters of the three GIBBERELLIN INSENSITIVE 1 genes (GID1a, GID1b, and GID1c) in Arabidopsis thaliana. Over the semester, students generated and analyzed gene expression data to understand the efficiency of twelve new gRNAs. We further validated CURE student-identified gRNAs with an undergraduate research assistant (URA) that assessed target gene expression and phenotypic outcomes in stable transgenic lines expressing SynTF constructs with the strongest gRNAs from the class. We further describe the curriculum structure to facilitate adoption at other institutions and present student-generated datasets demonstrating the utility of ViN-based screening for identifying effective SynTF gRNAs for plant functional genomics and engineering. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/715601v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@13869f5org.highwire.dtl.DTLVardef@b469feorg.highwire.dtl.DTLVardef@9aa51borg.highwire.dtl.DTLVardef@cdc129_HPS_FORMAT_FIGEXP M_FIG C_FIG

Combinatorial mutagenesis is essential for exploring protein sequence-function landscapes in engineering applications. However, while large-scale machine learning benchmarks exist for protein function prediction, they are primarily limited to single-mutant libraries, leaving a critical gap for combinatorial mutagenesis. Here we introduce CombinGym, a benchmarking platform featuring 14 curated combinatorial mutagenesis datasets spanning 9 proteins with diverse functional properties including binding affinity, fluorescence, and enzymatic activities. We evaluated nine machine learning algorithms from five methodological categories (alignment-based, protein language, structure-based, sequence-label, and substitution-based) across multiple prediction tasks, assessing both zero-shot and supervised learning performance using Spearmans {rho} and Normalized Discounted Cumulative Gain metrics. Our analysis reveals the substantial impact of measurement noise and data processing strategies on model performance. By implementing hierarchical dataset splits (0-vs-rest, 1-vs-rest, 2-vs-rest, and 3-vs-rest scenarios), we demonstrate the value of lower-order mutation data for empowering machine learning models to predict higher-order mutant properties. We validated this capacity through both in silico simulation (improving fluorescence brightness of an oxygen-independent fluorescent protein) and experimental validation (engineering enzyme substrate specificity), achieving a substantial increase in specific activity. All datasets, benchmarks, and metrics are available through an interactive website (https://www.combingym.org), facilitating collaborative dataset expansion and model development through integration with automated biofoundry platforms.

Precise regulation of gene expression in batch bacterial cultures is challenging because the underlying dynamics vary with cellular physiological state over time. Although cell-silicon systems enable rapid, real-time optogenetic control, disturbance rejection remains difficult in batch culture because the plant dynamics shift across growth phases, limiting the effectiveness of fixed-gain controllers designed under constant-growth assumptions. Here, we present a multiscale model-guided feedback control framework for disturbance rejection in batch E. coli cultures. Frequency-response analysis shows that the input-output dynamics of gene expression depend strongly on growth phase, revealing operating-point-dependent limits on the disturbance rejection performance of a fixed-gain PID controller. To address this limitation, we develop two growth-aware control strategies: a gain-scheduled PID (PID-GS) controller that adapts to cellular physiological state, and a gain-scheduled feedback-feedforward controller (PID-GS-FF) that further compensates for growth perturbations. We also introduce a controller evaluation framework that identifies three distinct operating regimes for targeted experimental validation. Together, these results show that accounting for growth-state-dependent dynamics is necessary for robust disturbance rejection in batch culture and provide a control-oriented framework for regulating living systems with shifting operating conditions.

Zymomonas mobilis is an ethanologenic Alphaproteobacterium with many interesting characteristics for fundamental research and applied microbial engineering. Although genetic engineering has been established for Z. mobilis since the 1980s, a rich set of inducible transcriptional regulators is still unavailable. In this work, seven different chemically inducible promoters have been systematically tested for their functionality in Z. mobilis. In particular, for the first time, NahR-PsalTTC, VanRAM-PvanCC, CinRAM-Pcin and LuxR-PluxB have been characterized in Z. mobilis, alongside the commonly used regulator-promoter pairs TetR-Ptet and LacI-PlacT7A1_O3O4, and the less commonly used XylS-Pm. All promoters investigated in this work are compatible with the Golden Gate modular cloning framework Zymo-Parts. Characterization was carried out with a shuttle vector backbone based on pZMO7, which has so far been rarely used for applications in Z. mobilis but seems to be completely stable without selection and generates high and uniform levels of expression. From the experimental results presented, it can be concluded that VanRAM-PvanCC and CinRAM-Pcin are particularly promising for broad use in the Z. mobilis community. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/712268v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@16579e6org.highwire.dtl.DTLVardef@1262533org.highwire.dtl.DTLVardef@15456a2org.highwire.dtl.DTLVardef@3af98_HPS_FORMAT_FIGEXP M_FIG C_FIG

Engineering bacterial promoters to integrate multiple regulatory signals remains a formidable challenge. Juxtaposing operator sites frequently increases basal leakiness, compresses the fully induced state, and introduces severe sequence-context dependencies. Here, we systematically engineered two-input combinatorial promoters in Escherichia coli that integrate signals from multiple transcription factors. To achieve precise operational control over these regulators, we drove the promoters using highly optimised, small-molecule-responsive sensors from the Marionette transcription-factor cassette, allowing us to assemble 19 reporter-specific, four-state truth tables across 12 distinct promoter architectures. We evaluated each design against a stringent statistical criterion for inducer-conditioned coincidence responses. Nine architectures satisfied this criterion, yielding a robust set of operational AND switches. By comparing successful and unsuccessful designs, we reveal that performance hinges primarily on suppressing partially induced states, ensuring structural compatibility between the promoter scaffold and the inserted operator, and precisely managing the orientation of long operators to avoid recreating unintended promoter-like motifs. Furthermore, reciprocal architectures and alternate downstream reporters frequently display divergent behaviours, underscoring profound asymmetries and local genetic-context dependencies. Ultimately, these findings deliver versatile combinatorial switches alongside practical, sequence-aware design rules for engineering multi-input bacterial promoters.

MotivationDespite continuing advances in sequencing and computational function determination, large parts of the studied gene, protein, and metabolite space remain functionally undetermined. Most function assignment is driven by homology searches and annotation transfer from known and extensively studied proteins but often fails to leverage available experimental omics data generated via technologies like mass-spectrometry. ResultsThe VaLPAS (Variation-Leveraged Phenomic Association Screen) framework is available as a Python package and provides a user-friendly platform for calculation of associations between expression patterns of genes or proteins in multi-omic datasets based on various statistical and learning methods. The goal of this approach is to shed light on the functional dark matter of protein space by elucidating previously unknown functions of molecules using guilt by association with molecules of known function. We present results demonstrating the utility of VaLPAS to identify high-confidence predictions for a subset of genes/proteins of unknown function in a previously published multi-omics dataset from the oleaginous yeast, Rhodotorula toruloides. AvailabilityVaLPAS is written in Python. The code is hosted on github (https://github.com/PNNL-Predictive-Phenomics/valpas/).

Cell-free expression systems (CFES) are increasingly used alongside conventional biotechnological approaches to accelerate early-stage prototyping and are particularly valuable in point-of-use settings. However, their broader adoption remains limited by time- and cost-intensive preparation, as well as stringent cryogenic storage requirements. To address this, several studies have explored lyophilization with protective additives to generate stable, solid-state CFES. These approaches had to balance the protection gained with a loss of activity due to the additives. In this study, we present a CFES that contains a tardigrade-derived Cytosolic-Abundant Heat-Soluble (CAHS) protein to protect the biosynthetic machinery in lysates from damages during drying. We show that the CAHS protein, without any other additives, preserves protein synthesis activity during low-cost room temperature desiccation, while unprotected lysates are affected in mRNA synthesis kinetics and translation yields. The diversity of tardigrade-derived protective proteins is a treasure trove for cell-free synthetic biology, in particular for making CFES more accessible and portable. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/715078v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@8ecc2eorg.highwire.dtl.DTLVardef@ff0432org.highwire.dtl.DTLVardef@6c940eorg.highwire.dtl.DTLVardef@6c5390_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background/ObjectivesRNA polymerase II is a multifunctional complex that is critical for gene regulation and environmental responses. Its POLR2I subunit in human is associated with various pathologies, including cancer chemoresistance. However, much of our understanding of how POLR2I could function indirectly derives from studies of its homologs in yeasts called Rpb9. Here, we endogenously humanized the rpb9 gene of the fission yeast Schizosaccharomyces pombe to examine the functional capabilities of POLR2I. MethodsWe edited the genomic rpb9 locus in S. pombe so that it encodes the human POLR2I protein, and investigated functional and structural conservation. ResultsWith our humanized yeast system, we find widespread functional complementation by human POLR2I of S. pombe rpb9 roles in yeast growth, chronological aging, and stress responses. We also find that POLR2I complements novel roles for yeast rpb9 in facultative heterochromatin assembly, resistance against the chemotherapy 5-fluorouracil, and resistance against the fungicide thiabendazole. In contrast, we find that POLR2I cannot complement the role of rpb9 in resistance against the transcription elongation inhibitor 6-azauracil (6-AU) in our system. Interestingly, POLR2I could complement 6-AU resistance if ectopically expressed. Lastly, we observe extensive structural homology between Rpb9 and POLR2I proteins. ConclusionsOur study establishes an endogenous cross-species gene complementation strategy that uncovers both conserved and rewired functions of fission yeast rpb9 and its human homolog, POLR2I. In addition to validating conserved roles, we also identified conservation of previously unrecognized roles of rpb9 in heterochromatin formation and chemoresistance.

The design of maximally divergent DNA sequences translating into the same protein is a critical problem in synthetic biology. Current design tools that rely on heuristics or machine learning often fail to effectively minimize the length of shared subsequences between the gene copies, compromising strain stability. Here, we introduce SIRIUS, a combinatorial optimization algorithm designed to generate maximally divergent coding sequences for a given protein of interest. Leveraging integer linear programming enforcing host-specific codon usage thresholds, SIRIUS stabilizes synthetic constructs and broadens the accessible design space for robust and scalable synethtic biology. Experimental results show that SIRIUS produces diverse sequences with fewer shared subsequences than existing methods. SIRIUS is freely available on GitHub at https://github.com/ucrbioinfo/sirius.

Designing next-generation Chimeric Antigen Receptors (CARs) requires a systematic understanding of intracellular signaling domains and their downstream biological effects, yet no comprehensive knowledge resource currently exists for this purpose. Here, we present an automated workflow that integrates multiple natural language processing and large language model tools to extract biomolecular interactions from PubMed literature and assemble them into a CAR T cell signaling knowledge graph. Our pipeline combines REACH, INDRA, and Llama 3 across 15 targeted search queries, yielding a directed multi-relational graph of [~]7,500 unique interactions among [~]1,800 entities, including proteins, biological processes, and chemicals. We further demonstrate that queries incorporating biological process ontology terms retrieve more interaction-rich papers than protein-name-only searches, offering practical guidance for future literature mining efforts. The resulting knowledge base provides a structured foundation for predicting T cell phenotypes and prioritizing intracellular domain candidates for CAR design, with broader applicability to knowledge-driven inference in immunotherapy research.

The softmax attention mechanism in transformer architectures (Vaswani et al., 2017) is mathematically identical to the Boltzmann distribution governing molecular binding at thermal equilibrium (Boltzmann, 1877). Luces Choice Axiom (1959) establishes this function - which we term the convergence equation - as the unique function satisfying five axioms of competitive selection: positivity, normalization, unrestricted domain, rank preservation, and independence of irrelevant alternatives. We show that five additional architecture conditions - discrete intermolecular contacts, bilinear energy decomposition, finite competitor pools, thermal equilibrium, and stochastic selection - are satisfied by at least ten biological molecular recognition systems and together prescribe a complete neural architecture: dual encoders, cross-attention, InfoNCE contrastive training, symmetric loss, learned temperature, and cross-attentive decoder. We term this architecture a Specificity Foundation Model (SFM) and specify it for antibody-antigen, TCR-peptide-MHC, transcription factor-DNA, microRNA-mRNA, enzyme-substrate, CRISPR guide RNA-DNA, drug-target, peptide-MHC, receptor-ligand, and RNA-binding protein-RNA recognition. The first implementation (CALM; Lee et al., 2026) achieves antibody-antigen retrieval from approximately 4,000 training pairs with [~]100,000-fold greater data efficiency than comparable contrastive architectures trained without the physics derivation. We classify this as Level 3 architecture-physics alignment and derive three further theoretical results: an exponential scaling law for retrieval accuracy as a function of training data diversity (the MRC scaling law), a two-parameter affinity calibration framework connecting contrastive scores to binding free energies, and a hybrid recursive learning framework for cross-modal reinforcement learning with orthogonal verification. The failure conditions of the framework are analyzed in terms of the validity of equilibrium thermodynamics for molecular binding and the convergence properties of gradient-based parameter estimation.

Despite >50 years of methods development, specific antibodies are still generated at low throughput and remain in high demand across biotechnology. Most biologics and immunoprobes are monoclonal antibodies, developed using a combination of inoculating animals with a target antigen, engineered candidate libraries, and multiple rounds of selection using phage or yeast display. Here we introduce a synthetic biology scheme to eliminate the need for nearly all of these steps, by combining Surface display on E. coli and Phage display with the microvirus {Phi}X174, Assisting Continuous Evolution (SurPhACE). Instead of building libraries for screening, SurPhACE runs a closed evolutionary program. A typical experiment can have 1011 mutant candidates under active selection, with complete turnover of the mutant population every 30min, or >5x1012 unique mutants per day, using less than 100mL of bacterial culture media. We demonstrate SurPhACE for optimizing a nanobody to a related epitope, and develop novel nanobodies for an arbitrary target using a minimal starting library to establish a proof of concept and identify best practices for this scalable method for generating protein binders.

Biocompatible fluorosurfactants are essential for many droplet microfluidic workflows but are often obtained from commercial sources because published syntheses of perfluoropolyether (PFPE)-based surfactants typically require acid chloride intermediates and chemistry-oriented purification methods. These requirements can limit access for biology and clinical laboratories seeking low-cost or customizable surfactant systems. Here we describe a practical method for preparing functional PFPE-based fluorosurfactant materials by direct carbodiimide coupling of functionalized PFPE carboxylic acids(Krytox 157 FSH) to amine-containing head groups under laboratory-accessible conditions. Using this approach, we prepared a PFPE-polyethylene-glycol (PFPE-PEG) material from Jeffamine ED900 and a PFPE-Tris material from Tris base. Because these products were not fully structurally characterized, we present them as functional reaction products and evaluate them by use in biomicrofluidic workflows rather than by definitive compositional assignment. PFPE-Tris was useful for generating relatively uniform small droplets, whereas the PFPE-PEG preparation supported a broader range of biological applications. These materials were used in genomic library screening for {beta}-glucosidase activity, thermocycling-associated droplet workflows, and protein crystallization experiments. In addition, the PFPE-PEG preparation improved emulsion behavior in many protein crystallization screens that were unstable with a commercial droplet oil used in our laboratory. This method reduces the practical barrier to in-house fluorosurfactant preparation and allows biology-focused laboratories to explore head-group chemistry, oil composition, and operating conditions without complete reliance on commercial reagents. The results support this workflow as a useful entry point for biomicrofluidics laboratories, while also highlighting the need for careful interpretation of thermocycled droplet assays and for future analytical characterization of the resulting materials. Significance statementDroplet microfluidics relies on fluorosurfactants that are often costly and difficult to synthesize outside of chemistry-focused settings. We describe a simple, biology-laboratory-compatible approach for generating functional perfluoropolyether-based fluorosurfactant materials using direct carbodiimide coupling and straightforward cleanup. The resulting materials supported multiple biomicrofluidic workflows in our laboratory, including enzymatic screening and protein crystallization, and provide a practical route for groups seeking lower-cost and more customizable surfactant systems.

In this study, we employ a two-stage dynamic metabolic control strategy to enhance the NADPH dependent biosynthesis of ethylene glycol from xylose in engineered E. coli. We evaluated the use of metabolic valves to dynamically reduce the enzymes involved in competitive pathways which compete for substrates with ethylene glycol biosynthesis, as well as regulatory pathways aimed at increasing NADPH fluxes. The performance of our initial strains with limits in pathway expression levels was improved by the addition of competitive valves, but not by increases in NADPH flux. In contrast, improving pathway expression levels, led to strains improved significantly by our regulatory valves which improved NADPH flux, but not by the competitive valves. This is consistent with a central hypothesis that faster pathways in and of themselves can compete with other metabolic fluxes by being faster and are better aided by regulatory changes capable of change rates elsewhere in metabolism. In this case in NADPH flux. Lastly, upon scale up to fed-batch bioreactors, our optimized strain, featuring dynamic control of two regulatory valves produced 140 g/L of EG in 70 hours at 92% of the theoretical yield.

Precise control of gene expression is essential for optimizing metabolic pathways, yet current tuning strategies based on promoter strength or gene copy number remain largely empirical. Chromosomal position represents an additional regulatory axis, as identical gene expression cassettes can exhibit markedly different expression levels depending on their integration sites. Here, we systematically quantified the expression output of 98 intergenic regions (IGRs) in Saccharomyces cerevisiae using a fluorescent reporter and developed a predictive framework, Yeast IGR Prophet (YeIP), that infers expression potential directly from genomic context. By integrating multi-scale genomic features including transcriptional neighborhood, chromatin state, and chromosome topology, YeIP accurately predicted expression ranks and enabled the construction of a genome-wide atlas of expression hotspot and coldspot. Using this atlas, we rationally optimized a three-gene lycopene pathway solely through genomic integration site selection, achieving optimal transcriptional stoichiometry without modifying promoters or gene copy numbers. These results transform chromosomal integration sites from static genomic coordinates into programmable regulatory elements, establishing a predictive, data-driven framework for rational and scalable design of metabolic pathways in yeast.

Probiotic-based encapsulation offers unique advantages over purified enzymes, such as increased protection from thermal-, pH-, and protease-mediated degradation, for oral therapeutic delivery applications. However, one of the major disadvantages of whole-cell systems is lower reaction rate due to substrate-product transport limitations imposed by the cell membrane and/or wall. In this work, we explore the potential of different lactic acid bacteria (LAB) - Lacticaseibacillus rhamnosus GG (LGG), Lactococcus lactis (Ll), and Lactiplantibacillus plantarum (Lp) - as expression hosts for recombinant Anabaena variabilis phenylalanine ammonia-lyase (AvPAL*). AvPAL* is used as a therapeutic to treat Phenylketonuria (PKU), a rare autosomal recessive metabolic disorder. Among the three species tested, LGG showed the highest PAL activity followed by L. lactis. Next, we attempted to overcome mass transfer limitation in whole-cell biocatalysts in two ways - expression of heterologous transporters and treatment with different chemical surfactants. Engineered strains expressing heterologous transporters exhibited approximately 3-4-fold increased PAL activity, while chemical treatment did not improve reaction rates. This work highlights the challenges and advances in realizing the potential of LAB as biotherapeutics. Impact StatementOral delivery of phenylalanine ammonia-lyase (PAL) using engineered probiotics is a promising therapeutic strategy to treat Phenylketonuria (PKU). Although PAL expression has been reported in probiotic strains of Limosilactobacillus reuteri, Lactococcus lactis, and E. coli, a systematic comparison of lactic acid bacteria (LAB) is underexplored. This study explores the potential of multiple LAB as hosts for PAL expression and investigates strategies to improve whole cell enzymatic activity. The findings from this study provide a foundation for implementing LAB-based delivery of PAL and indicate an important step towards development of probiotic platform for PKU management.

Cancer therapeutics are increasingly incorporating engineered receptors due to their ability to detect extracellular ligands and initiate intracellular responses that regulate gene expression. By redesigning these natural signaling systems, synthetic receptors hold great potential for use in novel cell-based therapies. One particularly promising direction is modifying the Notch receptor, a transmembrane protein that naturally mediates ligand-dependent signaling at the cell surface to regulate cell proliferation and differentiation in neurogenesis. Both the intracellular and extracellular domains of Notch can be replaced with alternative domains, creating the family of modified Notch receptors known as synthetic Notch (synNotch). In existing synNotch-activated chimeric antigen receptor (CAR) T-cells, the extracellular domain can be engineered to adjust binding affinity for a specific cancer antigen, enabling precise tuning of therapeutic activity while minimizing off-target effects. To quantify and inform such tuning, we develop differential equations models of synNotch receptor signaling and subsequent gene expression. The mathematical models couple activation dynamics on fast timescales (characteristic of receptor-ligand interactions) and on slow timescales (characteristic of downstream gene expression dynamics). Global Sobol sensitivity analysis of the proposed models highlights parameters that yield the greatest variability in synNotch signal transduction and gene expression, indicating their potential to be engineered for different functions in future cancer therapeutics. For the receptor-ligand interactions in the synNotch model, we find that ligand association and ligand-independent activation are the most sensitive parameters. In the downstream gene expression model, promoter strength and degradation rates of mRNA and gene product are found to be most amenable to engineering.

Some chimeric antigen receptor (CAR) T cell therapies have shown strong clinical efficacy, yet systematic screening of new CAR designs remains constrained by labor-intensive, low-throughput evaluation methods. To address this limitation, we developed a cytotoxicity-centered, high-throughput screening platform that integrates single-cell pooled screening with fully automated arrayed screening enabling both large-scale library handling and quantitative functional resolution for systematic CAR design exploration. Using a mutation-based CAR design approach guided by protein fitness prediction, we generated a 4-1BB-based CAR library with approximately 10 theoretical variants while minimizing the prevalence of low-activity designs. In pooled screening, CAR T cells were evaluated at the single-cell level based on cytotoxicity and proliferation, enabling rapid enrichment of high-performing variants from a highly diverse library. Subsequent automated arrayed screening quantitatively measured cytotoxicity with high reproducibility, providing high-resolution functional data suitable for comparative ranking. Selected CAR variants demonstrated superior antitumor efficacy in a leukemia xenograft model compared with a template CAR. Furthermore, systematic analysis of mutation sites from an enhanced CAR variant identified essential mutation combinations underlying functional enhancement. Together, this study establishes a cytotoxicity-focused screening framework that provides a robust approach for optimizing CAR architectures and accelerating the development of CAR T-cell therapies.

Microbes have the potential to manufacture plastics from sustainable feedstocks while enabling novel material properties and functions that are not easily accessible through conventional chemical synthesis. Realising this potential requires a comprehensive genetic and process engineering framework that spans chassis and bioprocess optimisation, polymer property control, and downstream functionalisation. Here we develop such a platform in Cupriavidus necator, with a focus on high-value polyhydroxyalkanoate (PHA) nanoparticles. To this end we first optimise the transformation protocol for the organism. Next, we create a library of PhaC synthase variants from C. necator, Aeromonas caviae and Brevundimonas sp. in a {Delta}phaC background, demonstrating that they allow customisation of the material properties of produced PHA particles. Our results combine data from Flow cytometry, Transmission Electron Microscopy (TEM), Fourier Transform InfraRed Spectroscopy (FTIR), and Differential Scanning Calorimetry (DSC) to show that it is possible to generate materials ranging from highly crystalline PHAs to softer P(3HB-co-3HHx) copolymers and that an A. caviae PhaC variant can double the yield of large PHA granules. To improve bioprocess sustainability, we coupled C. necator with B. subtilis in sucrose-fed co-cultures, using tetracycline tolerance differences and inoculation ratios to enhance PHA production from inexpensive, sugar-rich feedstocks. Finally, we add function to the produced PHA nanoparticles by using the molecular protein-fusion technology SpyTag-SpyCatcher, showing it is possible to efficiently capture SpyCatcher-GFP on PHA granules as a proof of concept for PHAs use as a customisable bio-based nanoparticle. Together, our work offers an innovation to produce bio-PHA nanoparticles in a customisable way, with potential applications in sustainable biomanufacturing, biosensing, drug delivery and future bioremediation technologies.