Synthetic Biology

{beta}-galactosidases (BGs) are essential enzymes widely used in the food industry, particularly in the production of lactose-free products. Among them, the BG from Aspergillus oryzae is of industrial relevance due to its activity at acidic pH and moderate thermal tolerance. However, enhancing its catalytic performance remains a key challenge. Traditional enzyme engineering methods are time-consuming and resource-intensive, limiting their scalability. Recent advances in Artificial Intelligence (AI), particularly those based on Natural Language Processing, offer a promising alternative by enabling efficient exploration of protein sequence space and prediction of beneficial mutations. In this study, we introduce an ensemble-based, zero-shot Protein Language Model pipeline that reconciles predictions from six independent models (ESM2 and the five ESM1v variants) combined with a diversity-aware candidate selection strategy. Applied to the BG from A. oryzae, this approach identified beneficial mutations leading to novel enzyme variants with up to a four-fold increase in catalytic efficiency on oNPGal, a two-fold increase on lactose, and, independently, a T338I variant with markedly enhanced thermostability ({approx}80% residual activity after 24 h at 60 {degrees}C), all without requiring supervised fine-tuning on experimental fitness data. Our results demonstrate that consensus across an ensemble of PLMs can efficiently enrich beneficial substitutions in industrially relevant enzymes and substantially reduce the number of wet-lab candidates that need to be screened. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/726739v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@18084f7org.highwire.dtl.DTLVardef@99a102org.highwire.dtl.DTLVardef@19a64forg.highwire.dtl.DTLVardef@1f59cff_HPS_FORMAT_FIGEXP M_FIG C_FIG

Recombinant human lactopontin (rhLPN), an equivalent of human milk lactopontin, is of increasing interest for human nutrition applications due to its roles in mineral binding, gastrointestinal function and immune modulation. These properties depend strongly on post-translational modifications, particularly phosphorylation and glycosylation. Here, we report the production of rhLPN in Kluyveromyces lactis at laboratory and pilot scale and present a comprehensive molecular comparison with native human lactopontin (nhLPN) isolated from human milk. Mass spectrometry-based peptide mapping confirmed the primary structure and identified extensive phosphorylation, consistent with the native protein. Middle-up analyses demonstrated closely matched phosphoform distributions between rhLPN and nhLPN, while glycosylation profiling revealed a defined population of low-complexity O-glycoforms localized to the N-terminus. Functional assessment demonstrated substantially greater iron binding by phosphorylated rhLPN compared with dephosphorylated and non-phosphorylated forms. Similar phosphorylation-dependent behaviour was observed for bovine lactopontin, supporting a conserved role for phosphorylation in mineral interaction. Across five 750 L pilot scale batches, both phosphorylation and glycoform distributions were highly consistent, indicating robust process reproducibility. Together, these results demonstrate that rhLPN produced in K. lactis recapitulates key structural and functional attributes of nhLPN, supporting its suitability as a scalable ingredient for nutrition applications.

Bacillus subtilis is an important chassis for biotechnology, but its use in multiplex genome engineering is limited by low natural transformation efficiency. Here, we compared inducible promoter systems for synthetic activation of the competence regulator ComK and evaluated their effects on the comG operon competence reporter and transformation efficiency. Xylose- and mannitol-inducible systems outperformed IPTG-based constructs and shifted 96-99% of cells into a reporter-positive competent state. However, reporter activation alone did not predict transformation potential. Optimization of culture density and induction timing increased transformant yield 45-fold relative to the initial protocol and 2800-fold relative to the conventional Spizizen method. Disruption of native competence regulatory genes did not improve performance and often reduced transformation output, highlighting the importance of endogenous regulatory circuitry. Using the optimized strain and protocol, we achieved co-transformation frequencies of 11-18% and constructed multiplex spore-display libraries containing fluorescent protein fusions integrated at multiple loci. Screening identified strong dual-display combinations and showed that cargo loading depends on anchor protein, integration locus, and genetic background. SscA fusions supported the highest display capacity and promoted synergistic co-display. Together, these results show improvements in natural transformation-based genome engineering in B. subtilis and provide insight into the construction of multifunctional engineered spores.

Improving the reconstituted translation system is a key requirement for bottom-up synthetic biology. Here, we developed a two-step in vitro evolutionary method that can be used for improving translational proteins. In this method, two distinct conditions were sequentially applied while maintaining genotype-phenotype linkage in water-in-oil droplets. Using this method, we performed in vitro evolution of four translation factors, IleRS, PheRS, EF-G, and EF-Tu, and identified mutations that modestly enhanced translation activity in in vitro expression assays. One of the EF-G mutations (P610S) increased activity per protein approximately 2-fold for the recombinant protein purified from E. coli. This selection method is useful for improving translational proteins for bottom-up synthetic biology.

Budding yeast Saccharomyces cerevisiae is a workhorse chassis for producing added food and agricultural compounds. However, building multi-enzymatic pathways for these chemicals often requires iterative genomic integration, underscoring the need for efficient, rapid genome-editing tools that can reliably target transcriptionally active chromosomal regions. In this study, to accelerate strain construction, we established a genome-editing toolkit to rapidly engineer eight loci, highly expressed hot-spots, but nonessential genomic sites suitable for stable pathway assembly. Our approach integrates three key design features: (i) selectable markers to enable rapid screening of edited cells, (ii) extended homology arms that leverage the yeast homology-directed repair machinery for robust genomic integration, and (iii) co-delivery of Cas9 and guide RNAs to promote efficient double-stranded DNA breaks at specific integration sites. The sequence independence of FASTOP relies on the release of integration cassettes from integrative vectors, mediated by restriction digestion at two flanking multiple-cutting sites in the integration module to minimize the risk of introducing sequence errors during PCR amplification of the integration cassettes. Following the introduction of a fluorescent reporter cassette, we observed high integration efficiencies across the target sites. We then integrated the biosynthetic pathway of plant-derived flavonoid naringenin into the hot-spots of the yeast genome using the FASTOP toolkit. Our results demonstrated that upon expressing the five essential genes in simple shake flask culture, naringenin production reached 505.7 mg/L, representing a significant (69-fold) increase over previously reported titers for comparable minimal heterologous pathways in S. cerevisiae. Together, the FATSOP toolkit provides a user-friendly platform for reliably modifying hot-spot loci to rapidly construct multi-enzymatic metabolic pathways in S. cerevisiae, while achieving high production levels for high-value food-relevant metabolites.

Fluorescent reporters cover a wide range of applications in both basic and applied research. Whether a study involves microscopic imaging to study (co)-localization of proteins, FRET, biosensing, or quantifying gene expression, fluorophores are attractive reporter candidates due to their relatively straightforward in vivo readout. For microbiological applications, a wide variety of fluorescent proteins with varying excitation and emission wavelengths, brightness levels, and maturation times are available. Careful consideration is required when selecting from this large suite of proteins, especially when choosing multiple fluorophores. This is further complicated in phototrophic organisms, which exhibit strong autofluorescence, especially towards the red part of the spectrum, effectively eliminating common candidates such as mCherry. In this study, the specific properties and performance of a selection of fluorescent proteins are systematically evaluated against the background of photosynthetic pigment-derived autofluorescence in the cyanobacterium Synechocystis sp. PCC 6803. Specific readouts of different combinations of fluorescent proteins are also analyzed using high-throughput methods, namely plate reader fluorescent scans and single-cell flow cytometry to quantify fluorescence. The ultimate goal is to assess each fluorescent protein with regard to: 1.) Its ability to be discerned from cyanobacterial autofluorescence. 2.) Its compatibility with other fluorophores in this context. 3.) Its overall suitability in cyanobacterial research. Several highly suitable fluorescent proteins for use in cyanobacteria are identified, including mTagBFP2, mNeonGreen and mScarlet-I and suitable combinations, covering nearly the whole spectrum of visible light. This study expands the knowledge and toolset for current and future researchers and uncovers a whole spectrum of possibilities for fluorescent protein selection in cyanobacterial cell biology.

Rapid and specific diagnosis of viral and bacterial infections is a significant challenge in medicine and veterinary science, especially in the case of epidemically dangerous pathogens. The African swine fever virus (ASFV), for example, causes annual outbreaks among livestock, resulting in significant economic losses for farmers. DNA aptamers have been identified as a promising tool for point-of-care diagnostics, being highly specific to the target and stable ambient temperatures during storage. In this study, we describe the selection of DNA aptamers targeting the p54 viral protein using a single-round selection process. These aptamers were able to bind both to recombinant protein and inactivated ASFV viral particles. Analysis of the newly generated aptamers revealed a dependence of affinity and thermal stability on Ni2+ content, which was a dopant in the selection process. In some cases, the affinity increased 100 times, and melting temperature increased by 30{degrees}C. We have identify two novel DNA motifs that bound 2-3 Ni2+ or Zn2+ ions.

This protocol details the extraction of high-molecular-weight genomic DNA from grapevine tissues (wild and cultivated Vitis spp., including pathogen-infected samples) and the subsequent preparation of Illumina(R) whole-genome sequencing libraries using bead-bound Tn5 transposase. It is designed to overcome challenges from polyphenolic compounds and secondary metabolites in wild plants, providing a cost-effective workflow for large-scale population genomics. It includes recipes for buffers, incubation times, critical notes, and troubleshooting tips to maximize yield and library quality. Although designed for the grapevine DNA, this protocol is potentially applicable to other similar wild plant species HighlightsO_LIOptimized CTAB-PTB DNA extraction protocol for field-collected wild plant tissues. C_LIO_LIEffective removal of polyphenols and secondary metabolites associated with DNA using PTB. C_LIO_LICost-effective Illumina DNA Prep library preparation using bead-bound Tn5 transposase (Tagmentation). C_LIO_LIScalable workflow suitable for large-scale population genomics in Vitis species. C_LIO_LIValidated method for high-molecular-weight DNA and high-quality sequencing data. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/713680v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@b637d4org.highwire.dtl.DTLVardef@10b563aorg.highwire.dtl.DTLVardef@14a32caorg.highwire.dtl.DTLVardef@4c9577_HPS_FORMAT_FIGEXP M_FIG C_FIG

The yeast secreted proteome plays critical biological roles and influences product and production parameters in industrial fermentation. Systematic profiling of the response of the yeast secretome to intrinsic and extrinsic factors is therefore essential for understanding these functions and for optimizing manufacturing processes. Here, we characterized the yeast secretome under diverse proteosynthetic stress conditions, including glycosylation deficiency, oxidative, reductive, and thermal stresses. The secretome was predominantly composed of conventionally secreted proteins, while a subset of proteins appeared to be secreted via unconventional pathways. Distinct secretome profiles were observed in response to different stressors, driven by a combination of altered intracellular proteomes, altered canonical secretion, and altered cell lysis and unconventional protein secretion, while reflecting the underlying metabolic state of the cells. Heat stress did not impact protein glycosylation but did cause similar protein misfolding stress to N-glycosylation deficiency. Intriguingly, canonically intracellular chaperone BiP was abundant in the secretome in particular stress conditions where its activity would be beneficial. BiP interacted with probable extracellular client proteins in vitro, consistent with it acting as a functional extracellular chaperone/holdase in conditions such as reductive stress in which client proteins could be misfolded outside the cell.

Temporal gradient sensing is a fundamental capability observed across diverse natural biological systems, contributing to the coordination of their functions. Harnessing this ability is also of significant interest in synthetic biology, particularly for sensing and control applications. In this work, we focus on a biomolecular topology that exemplifies a broader class of signal-differentiating architectures, while introducing a structural variant of it. We examine their behavior under both nominal and non-ideal conditions, accounting for stochastic noise arising from different sources. Our investigation includes scenarios where these topologies operate independently, as well as when embedded within minimal regulatory architectures based on negative as well as positive feedback. We analyze the stability of the resulting macroscopic dynamics--a prerequisite for practical deployment--and quantify stochastic fluctuations in system output, providing comparisons with the corresponding input/unregulated process. Importantly, our results demonstrate that signal differentiation can be effectively implemented in a biomolecular setting without incurring deleterious noise amplification--a major concern in the utilization of derivative action across disciplines.

DNA offers exceptional information density and stability, making it a promising medium for long-term data storage. However, the high cost of DNA synthesis and data retrieval remain key barriers to large-scale deployment. In this paper, we present DNA staples, a multipurpose library of short single-stranded DNA sequences that enables the encoding of arbitrary digital data by enzymatic assembly. Flexible encoding schemes allow the same presynthesized strand library to be used across applications, significantly reducing synthesis requirements while supporting diverse data representations. Using a restricted library also confers inherent error correction. In addition to storage, the library enables creation of computational DNA modules that perform highly parallel operations directly on stored data. This framework provides a cost-efficient approach to molecular data storage and supports integrated storage-computation at the DNA level.

Expanding genetic engineering beyond model microorganisms is critical to unlocking novel applications in biotechnology, yet the low efficiency of DNA delivery methods like conjugation, remains a major bottleneck in non-model and environmental microbes. Here, we present an automated, high-throughput droplet microfluidic platform that enhances conjugation by encapsulating donor and recipient microbes in picoliter-scale water-in-oil microdroplets, stabilizing cell-cell contact and DNA transfer. Optimization of incubation time, donor to recipient ratio, and plasmid type yielded over a 100-fold increase in conjugation efficiency compared to conventional methods and enabled delivery of complex DNA libraries in low reaction volumes, demonstrating scalability for pooled plasmid library delivery. We further utilized a synthetic biology circuit for donor removal within microdroplets without antibiotic selection, eliminating the need for host-specific selection markers or engineered auxotrophs. When applied to a soil microbial community, this platform improved community-level conjugation, preserving microbial diversity and enabling the identification of genetically accessible chassis. Collectively, this platform establishes a scalable, generalizable solution for high throughput DNA delivery in previously inaccessible microbial hosts. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=54 SRC="FIGDIR/small/721591v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@c7a8d4org.highwire.dtl.DTLVardef@1d1fbaorg.highwire.dtl.DTLVardef@e1faforg.highwire.dtl.DTLVardef@14234dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Campylobacter jejuni is a major cause of food-borne gastroenteritis and is responsible for substantial mortality and economic losses in meat and dairy production. Detecting C. jejuni in contaminated food samples remains difficult because current assays are culture-based, slow, and can yield false positives. As a result, contamination may not be identified for several days, limiting detection at the point of production. Developing improved assays has also been challenging because Campylobacter genetics and the biology of clinical isolates remain poorly understood. Here, we expand the C. jejuni genetic toolbox by sequencing two strains, HC1 and RM1164, derived from patient and food samples. We identified two cryptic plasmids in HC1, one potentially capable of conjugation and another conferring tetracycline resistance. We also engineered a mobilizable plasmid carrying an OriT sequence that can be transferred from Escherichia coli donor strains to C. jejuni RM1164 by conjugation. Together, these clinical isolates and the plasmid system expand the genetic tools available for C. jejuni.

Engineered microbial communities hold significant biotechnological potential because their collective metabolism can produce functions beyond those achievable by individual strains. However, multicellular synthetic gene circuits require orthogonal communication systems that enable precise, programmable signaling between cells. Quorum sensing (QS), where cells both produce and detect small diffusible signal molecules, offers a natural framework for such intercellular communication. However, the construction of complex multicellular circuits for applications such as biobased production is currently hampered by the limited number of orthogonal QS channels available in yeast. Here, we expand the QS toolkit in Saccharomyces cerevisiae by characterizing four LuxR-type biosensors based on EsaR, LasR, TraR and RpaR, alongside the previously established LuxR biosensor. We functionally expressed acyl-CoA-dependent HSL synthases in yeast, producing a diverse range of aliphatic and aromatic HSL signals. LuxR and RpaR, were compatible with in vivo ligand production and established as orthogonal QS signaling pair with synthases MesI and RpaI, respectively. Co-culture experiments demonstrated QS-dependent intercellular signaling, with 3.9-fold and 6.4-fold induction relative to monocultures. Together, these results establish a modular and extensible platform for orthogonal intercellular communication in yeast, enabling the construction of multicellular synthetic gene circuits.

GPCR oligomerization has been reported for decades, yet its extent and functional relevance in living cells remain unresolved because existing approaches, often done in bulk, are poorly account for local receptor density, a major determinant of intermolecular interactions. Here, we establish a generic quantitative imaging framework that links spatially resolved FRET measurements describing protein oligomerization to local membrane protein in living cells. Using automated high-throughput analysis of fluorescence images, the method generates large density-resolved datasets that enable direct quantification of receptor oligomerization parameters, including apparent affinity, oligomerization state, and monomer/dimer populations at the submicrometer scale. Applied to class A GPCRs in HEK293 cells, the approach reveals receptor-specific density-dependent equilibria between monomers and dimers over physiologically relevant expression ranges, with no evidence for stable higher-order oligomers under basal conditions. The receptors studied exhibit distinct apparent affinities for dimerization, ranging from predominantly monomeric to dynamic monomer-dimer equilibria, indicating that local membrane density strongly influences receptor organization and that it is receptor dependent. The agreement between our measurements and low-density single-molecule studies further suggests that previously reported higher-order oligomers may partly reflect density-driven receptor proximity effects. By bridging single-molecule and ensemble measurements within a unified quantitative framework, this work reconciles conflicting observations in the GPCR oligomerization literature and provides a broadly applicable strategy for investigating membrane protein organization in living cells. SignificanceGPCR oligomerization in living cells is strongly influenced by the local protein density, yet most approaches do not quantitatively account for this parameter. Here, we introduce a quantitative high-throughput imaging framework that directly relates membrane protein local density to local oligomerization state in living cells. Applied to distinct GPCRs over physiologically relevant density ranges, the method reveals distinct density-dependent monomer-dimer equilibrium and apparent affinities for self-association. These results help reconcile longstanding discrepancies, where distinct oligomerization states have been measured depending on experimental conditions. More broadly, this work establishes local membrane protein density as a key determinant of membrane protein organization, and provides a quantitative framework applicable to membrane protein complexes in their native cellular context.

Wickerhamomyces ciferrii is a non-model diploid yeast that naturally produces tetraacetyl phytosphingosine (TAPS), a sphingoid base used in cosmetic and dermatological applications. However, its strong preference for non-homologous end joining (NHEJ) over homologous recombination (HR) limits conventional genome editing, while disruption of LIG4, a core NHEJ gene, compromises cellular fitness. Here, we repurposed native NHEJ activity to develop a homology-independent multicopy genome integration platform for W. ciferrii. The platform combines three optimized donor-design features: telomeric end-shielding with two tandem copies of an 11 bp repeat to improve linear donor persistence, a defective URA5 auxotrophic marker to enrich multicopy integrants, and 5'-phosphorylated donor termini to enhance transformant recovery and integration output. These features were consolidated into the platform vector pTdmVU5. As a metabolic engineering demonstration, multicopy integration of LCB1 and LCB2, encoding the two subunits of serine palmitoyltransferase, increased TAPS titer by 2.7-fold. This work converts the native NHEJ bias of W. ciferrii from a barrier to precise genome editing into a practical tool for pathway amplification and establishes a framework for engineering NHEJ-dominant non-model yeasts.

Artificial intelligence has advanced from analyzing experimental data to autonomously generating hypotheses, designing experiments, and coordinating closed loop discovery. Yet the translation from computational reasoning to physical execution remains bottlenecked by the experimental protocol, which in biology still relies on ambiguous natural-language descriptions: a medium other engineering disciplines abandoned decades ago in favor of compiler verified specification languages. This deficit fragments reproducibility along three axes: protocol accuracy, pre execution verification, and cross platform portability. Existing formalisms address only subsets of these challenges, trading expressiveness for rigor, portability for standardization, or usability for provenance. Here we introduce the Biology Protocol Language (BPL), a domain specific language with a biology-native type system in which every quantity carries physical units, every reagent declares its physical form, and every container maintains compiler-tracked state, so that implicit assumptions must be stated explicitly and physically impossible operations are rejected at compile time. We further develop BPL-COGEN, a pipeline that couples a fine tuned 30 billion parameter language model with the deterministic compiler in a closed generate validate repair loop, iteratively correcting the translation from natural language SOPs to BPL through compiler diagnostics until all physical, dimensional, and state constraints are satisfied. On a benchmark of 300 published Nature Protocols papers, BPL COGEN achieved an overall fidelity score of 95.1 against the source protocols as ground truth. Wet-lab experiment and cross-platform validation in GFP expression library construction and HPLC to UHPLC method translation confirmed that a single BPL source yielded reproducible execution across manual and liquid handler assisted contexts. The results established a novel pipeline that generates compiler-verified protocols, which is an essential prerequisite for physically embodied AI in biology.

Sponge RNAs (spRNAs) play an important regulatory role in bacterial small RNA (sRNA) networks, but their engineering and quantitative systems-level properties are unexplored. Here, we design, build, and quantitatively characterise synthetic spRNA-based gene circuits in E. coli. We establish multiple design strategies for synthetic spRNAs, engineering the first synthetic spRNAs. We show that these synthetic spRNAs can reversibly de-repress sRNA-regulated gene expression, demonstrate tuneable control of gene expression, and extend these designs to multi-target regulation. Through the use of time-resolved continuous-culture characterisation in Chi.Bio together with absolute fluorescent protein quantification, we generated a quantitative dynamical dataset for model fitting and mechanistic analysis. Sequential model development showed that recapitulating the observed circuit dynamics required incorporation of Hfq-mediated resource competition, often overlooked in models of sRNA-based synthetic gene circuits. The extended model captured promoter, sRNA, and sponge circuit behaviour and was used to investigate quantitative properties of spRNA-mediated regulation, the first such quantitative investigation of spRNA-based regulation. Model-based quantitative investigations further suggest that spRNAs can tune response functions, modulate thresholds and leakiness, alter response times, improve disturbance rejection in some regimes, increase effective specificity, and buffer regulatory output against sRNA mutation. Together, these results establish synthetic spRNAs as a new post-transcriptional tool for bacterial synthetic biology and provide a quantitative framework for understanding natural and engineered spRNA-mediated regulation.

Cyanobacterial natural products are a rich source of bioactive compounds, yet their heterologous production remains challenging. This study investigates the feasibility of expressing the lyngbyatoxin A (LTXA) biosynthetic gene cluster in a fungal host. The lyngbyatoxin biosynthetic genes (ltxA, ltxB, ltxC) were individually cloned and expressed in Aspergillus oryzae NSAR1 under the control of an inducible promoter. Metabolite production was assessed using LC- MS, and transcriptional analysis was performed by RT-PCR. Codon-optimized constructs and precursor feeding experiments were employed to evaluate pathway functionality. No production of LTXA or pathway intermediates was detected upon co-expression of ltxA-C despite confirmed transcription of ltxB and ltxC. RT-PCR analysis revealed truncation of the ltxA transcript, suggesting incompatibility with fungal transcriptional or splicing machinery. In contrast, expression of a codon-optimized ltxC enabled biotransformation of indolactam V to LTXA in A. oryzae, confirming functional expression of the prenyltransferase. These results highlight transcriptional limitations as a key barrier to heterologous expression of cyanobacterial NRPS pathways in fungal hosts, while demonstrating that downstream tailoring enzymes can remain functional. This work provides insights for future engineering of fungal platforms for cyanobacterial natural product biosynthesis.

Designing minimal biological systems with emergent functions such as spatiotemporal self-organization is a central goal of bottom-up synthetic biology. While computational optimization and design show promises to accelerate functional protein engineering through Design-Build-Test-Learn cycles, screening libraries for complex functions remains a major challenge. Conventional screens typically lack the spatiotemporal resolution and cell-like confinement required in bottom-up synthetic biology. Here, we present PUREdrop, an automated microfluidic platform that encapsulates and expresses protein libraries in thousands of picolitre-sized synthetic cells per construct. These droplets are sorted into a 96-well plate and analyzed by time-lapse imaging, allowing parallel quantification of expression kinetics and emergent functions. To demonstrate the platforms potential, we first screened computationally re-designed variants of the bacterial cell division protein FtsZ, identifying variants with improved bundling phenotypes and faster kinetics. We then extended our screening procedure towards general protein modulators of FtsZ and identified a combination that anchors filaments to the interface, producing a ring-like phenotype. PUREdrop bridges computational protein engineering and synthetic cell research, elevating the rational engineering of complex biological function to the next level.