39 papers
Synthetic biology is like engineering for life, where scientists design and build new biological parts or rewire existing ones to solve real-world problems. Instead of just studying how nature works, this field asks what we can create, from bacteria that produce biofuels to smart materials that heal themselves. It sits at the exciting intersection of biology, engineering, and computer science, turning the code of life into something we can read, edit, and program.
At Gist.Science, we bring you the very latest discoveries in this rapidly evolving space directly from bioRxiv. We process every new preprint in this category as soon as it appears, offering both plain-language explanations for the curious mind and detailed technical summaries for researchers. This ensures you never miss a breakthrough, regardless of your background or how deep you want to dive into the science.
Below are the newest preprints in synthetic biology, carefully curated and summarized just for you.
This paper introduces CombinGym, a benchmark platform featuring 14 curated datasets and a comprehensive evaluation of machine learning algorithms to address the gap in combinatorial protein design, demonstrating that leveraging lower-order mutation data significantly improves the prediction and experimental engineering of higher-order protein variants.
This study presents a systematic engineering approach in *Escherichia coli* that successfully creates robust two-input AND logic switches by optimizing transcription factor sensors and establishing critical design rules to suppress leakiness and manage sequence-context dependencies in combinatorial promoters.
This study demonstrates that while *Lacticaseibacillus rhamnosus* GG is the most effective host for expressing therapeutic phenylalanine ammonia-lyase, its activity is primarily limited by substrate transport, which can be significantly enhanced (3–4-fold) through the expression of heterologous transporters rather than chemical surfactant treatment.
The Cepeda Framework is a modular, safety-first preclinical architecture that integrates five regulatory principles and five companion protocols to coordinate partial rejuvenation across all twelve official hallmarks of aging, utilizing a 3:2:1 OCT4:SOX2:KLF4 stoichiometric ratio and a nine-safeguard defense-in-depth system validated through 21,000 computational simulation cycles to ensure efficacy while preventing uncontrolled pluripotency.
This paper demonstrates that engineered *E. coli* can implement a genetically encoded local learning rule using plasmid copy-number ratios as persistent memory, enabling physical learning and supervised adaptation in single and multicellular bacterial populations for applications ranging from logic gates to autonomous biological hardware.
This study establishes a comprehensive genetic and process engineering platform in *Cupriavidus necator* that enables the customizable biomanufacturing of functionalized PHA nanoparticles with tunable material properties, enhanced production yields via co-culturing, and surface bioconjugation capabilities for diverse applications in biomedicine and sustainability.
This study demonstrates that basal Xenobots, synthetic living constructs derived from Xenopus embryonic cells, can exhibit distinct, long-term, stimulus-specific memories through coordinated ciliary activity, calcium signaling, and transcriptional changes following exposure to specific chemical stimuli, thereby establishing a foundation for understanding non-neural information processing in synthetic cellular collectives.
This study demonstrates a selection-free whole genome transplantation method that revives chemically inactivated dead bacterial cells by installing a synthetic genome, thereby creating the first living synthetic cell from non-living parts and overcoming previous barriers to expanding this technology across diverse bacterial species.
This study demonstrates that a variational autoencoder trained on LuxR-family DNA-binding domains can successfully generate hybrid transcriptional activator proteins with novel, dual-promoter recognition capabilities, offering a data-driven strategy to expand the design space of synthetic genetic circuits.
This study presents Fung-AI, an AI/ML-driven pipeline that successfully generated and experimentally validated novel de novo antifungal peptides with activity against *Fusarium graminearum* and *Candida albicans* and low cytotoxicity, demonstrating a proof-of-concept for rapid antifungal discovery.