55 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 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.
This study demonstrates that current genomic language models fail to generate realistic synthetic genomes because they capture local sequence statistics but fundamentally lack the ability to preserve essential long-range organization, repetitive elements, and evolutionary constraints, making synthetic sequences easily distinguishable from natural ones.
This study demonstrates that experimentally expanding training data diversity through large-scale gene synthesis and DNA shuffling enables machine learning models to overcome extrapolation limitations, successfully guiding the discovery of novel functional fluorescent proteins in previously unexplored regions of sequence space.
This study demonstrates that AI-assisted verification, specifically using the Gemini 2.5 Pro model integrated with external APIs, can achieve accuracy comparable to human experts in screening synthetic nucleic acid orders while reducing costs by up to 50-fold, supporting a hybrid approach where AI handles information gathering and humans retain final decision-making authority.
This study utilizes engineered *Escherichia coli* whole-cell biosensors to map the complex, spatially variable bioavailability of host-derived sialic acids within the inflamed mammalian gut, revealing distinct regional dynamics between metabolite availability and inflammation while demonstrating the utility of this approach for monitoring rapid metabolic turnover and guiding therapeutic interventions.
This study demonstrates a combinatorial optimization strategy for synthetic cells by screening large populations of vesicles with varied translation rates across multiple genes, successfully isolating high-performing variants for both DNA self-replication and phospholipid synthesis pathways while revealing the predictive limits of single-mutation data for complex multi-gene systems.
This paper presents a significantly upgraded, portable orthogonal replication system (EcORep and VinORep) that drives continuous gene evolution near the biological speed limit by enabling ultra-high mutation rates and rapid functional optimization in *E. coli* and *Vibrio natriegens*.
The paper introduces CyanOperon, an expansion of the CyanoGate MoClo toolkit that enables the hierarchical assembly and expression of synthetic operons with up to six genes in both *E. coli* and *Synechocystis* sp. PCC 6803, demonstrated through violacein production, RBS spacer optimization, and comparative expression studies.
This study demonstrates that programmable, phase-separated RNA-protein sialogranules can be engineered to display high-avidity sialic acid ligands, enabling them to sense lectins and function as effective antiviral decoys that inhibit influenza virus entry.