Intercellular communication is essential for distributed genetic circuits operating across cells in multicellular consortia. While diverse signalling molecules have been employed--ranging from quorum sensing signals, secondary metabolites, and pheromones to peptides, and nucleic acids--phage-packaged DNA offers a highly programmable method for communicating information between cells. Here, we present a library of five M13 phagemid variants with distinct replication origins, including those based on the Standard European Vector Architecture (SEVA) family, designed to tune the growth and secretion dynamics of sender strains. We systematically characterize how intracellular phagemid copy number varies with cellular growth physiology and how this, in turn, affects phage secretion rates. In co-cultures, these dynamics influence resource competition and modulate communication outcomes between sender and receiver cells. Leveraging the intercellular CRISPR interference (i-CRISPRi) system, we quantify phagemid transfer frequencies and identify rapid-transfer variants that enable efficient, low-burden communication. The phagemid toolbox developed here expands the repertoire of available phagemids for DNA-payload delivery applications and for implementing intercellular communication in multicellular circuits.

Enhancements to crop morphology, such as the semi-dwarfing that helped drive the green revolution, are often driven by changes in gene dosage. These changes are challenging to translate across varieties and species, which slows the pace of crop improvement. Synthetic transcription factors (SynTFs) offer a rapid alternative to generate targeted alterations to gene dosage. However, the complexity of developmental pathways makes it unclear how to best apply them to predictably engineer morphology. In this work, we explore if mathematical modeling can guide SynTF-based expression modulation of genes in the signaling pathway of the phytohormone, gibberellin (GA), which is a central regulator of cell expansion, to elucidate the design principles for engineering organ size. We demonstrate that modulation of GA signaling gene expression can generate consistent dwarfing across tissues in both controlled and variable environments in the model plant Arabidopsis thaliana, and that the degree of dwarfing is dependent on the strength of regulation as predicted by modeling. We further validate the predictive power of the model by demonstrating its capacity to accurately predict the qualitative impacts of different regulatory architectures for both increasing and decreasing organ size. Finally, we show that these insights can be generalized for engineering organ size in the crop Solanum lycopersicum (tomato). This work creates a framework for predictable engineering of an agriculturally important trait, organ size, across tissues and plant species. It also serves as a proof-of-concept for how mathematical models can guide SynTF-based alterations in gene dosage to enable bottom-up design of plant phenotypes.

Promoters responsive to changes in cultivation conditions are essential tools for dynamic metabolic engineering. Oxygen-responsive promoters, in particular, exhibit significant application potential in oxygen-limited fermentation processes. However, currently reported oxygen-dependent promoters exhibit limited dynamic ranges, and notably, there remains a lack of research on oxygen-responsive negatively regulated promoters. In this study, we designed and characterized a series of dissolved oxygen-responsive promoters in Escherichia coli under the regulation of the transcription factor fumarate-nitrate reduction (FNR). Anaerobically activated promoters were constructed by inserting FNR binding site (FBS) upstream of inducible core promoters, while anaerobically repressed promoters were developed by inserting FBS downstream of or flanking constitutive promoters. The most effective anaerobically activated promoters showed 24–138-fold higher activity under anaerobic conditions compared to aerobic conditions. Under anaerobic conditions, promoters with DNA looping-mediated anaerobic repression maintained only 8–17% of the activity observed under aerobic conditions. These promoters were specifically regulated by FNR, as confirmed by tests in a DH5α Δfnr strain, and responded rapidly to oxygen depletion (within 30 min). The utility of these genetic tools was demonstrated by applying them to enhance pyruvate production in E. coli. An engineered strain with anaerobic-repressed aceE and anaerobic-activated atpAGD genes produced 5.76 g/L pyruvate at 55.7% yield in shake flask fermentations. This study offers an expanded toolbox of oxygen-responsive promoters that enable precise gene regulation based on dissolved oxygen levels, providing novel genetic strategies for developing efficient two-stage fermentation processes with separated growth and production phases.

We compared the performance of three widely used protein structure prediction tools - AlphaFold2, ESMFold, and OmegaFold - using a dataset of over 1,300 newly created records from the PDB database. These structures, resolved between July 2022 and July 2024, ensure unbiased evaluation, as they were unavailable during the training of these tools. Using metrics such as root mean square deviation (RMSD), template modeling score (TM-score), and predicted local distance difference test (pLDDT), we found that AlphaFold2 consistently achieves the highest accuracy but depends on high-quality sequence alignments. In contrast, ESMFold and OmegaFold provide faster predictions and excel in challenging cases, such as rapidly evolving or designed proteins with limited sequence homology. Comparing ESMFold and OmegaFold, ESMFold achieves higher confidence scores (pLDDT) and structural similarity (TM-score). OmegaFold is competitive in specific contexts, such as de novo-designed proteins or sequences with limited evolutionary information. Additionally, we demonstrate that machine learning models trained on protein language model embeddings and pLDDT confidence scores can predict potential structure prediction failures, helping to identify challenging cases early in the pipeline.

Protein-protein conjugation systems are a powerful way of creating fusion proteins and enable the dynamic combination of protein domains with diverse functionalities. However, the insertion of these systems into enzymes is often performed with little consideration of the structural impact they might have. This is particularly relevant when modifying complex molecular machines that transition between numerous conformational states. Here, we address this issue by developing SIMPLIFE, a computational workflow that supports the design of optimal insertion sites for conjugation tags based on the structure of the proteins involved and performs localised residue redesign where needed. We demonstrate how SIMPLIFE can be used to effectively augment the function of T7 RNA polymerase using the DogCatcher-DogTag system, enabling diverse and dynamically varying mutations within a targeted region of DNA. This work demonstrates the power of combining biophysical and machine learning based approaches for protein structure prediction to efficiently augment the function of molecular machines, accelerating our ability to combine complex biochemical functionalities in new ways.

Regulating gene expression with precision is essential for cellular engineering and biosensing applications, where rapid, programmable, and sensitive control is desired. Current approaches to regulatory circuit design often rely on control at a single regulatory level, primarily the transcriptional level, thereby limiting the capability of fine-tuning the regulatory dynamics in response to complex stimuli. To address this challenge, we developed four novel RNA-protein hybrid type-1 incoherent feed-forward loop (I1-FFL) circuits in Escherichia coli that integrate transcriptional and translational regulators to achieve multi-level control of gene expression. These hybrid circuits leverage the modularity and rapid dynamics of RNA-based activators alongside the versatile inhibition capabilities of the protein-based repressors, to endow tunable pulse dynamics through engineered delays that act as transient repressor decoys. By repurposing synthetic RNA regulators at multiple regulatory levels together with aptamer and RNA-binding proteins, we demonstrate previously unexplored circuits with tunable dynamics. Complementary simulation results highlighted the importance of the engineered delays in achieving tunable pulse dynamics in these circuits. Integrating modeling insights with experimental validation, we demonstrated the flexibility of designing the RNA-protein hybrid I1-FFL circuits, as well as the tunability of their dynamics, highlighting their suitability for applications in environmental monitoring, metabolic engineering, and other engineered biological systems, where precise temporal control and adaptable gene regulation are desired.