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 study systematically quantifies the degradation of 103 heterologous enzymes in *Synechocystis* sp. PCC 6803 using a novel split-GFP and CRISPRi approach, revealing that nearly half of these proteins are significantly degraded and demonstrating that swapping enzymes for homologs is often more effective than optimizing genetic elements for improving expression in cyanobacterial cell factories.
The paper introduces SIRIUS, a combinatorial optimization algorithm that uses integer linear programming to generate maximally divergent DNA sequences for a given protein while adhering to host-specific codon usage constraints, thereby outperforming existing heuristic and machine learning methods in minimizing shared subsequences to enhance synthetic strain stability.
This paper presents an automated workflow integrating NLP tools and large language models to construct a comprehensive knowledge graph of CAR T cell signaling interactions from PubMed literature, thereby providing a structured resource to guide the design of next-generation chimeric antigen receptors.
This study employs quantitative PCR to globally analyze individual ligation reactions within DNA origami nanostructures, revealing that ligation efficiency is spatially dependent on ligase docking probability at edges versus inner sites, a trend that can be abolished by DMSO co-solvents, thereby providing critical insights for optimizing DNA nanostructure stability for real-world applications.
This paper develops a multi-scale mathematical model and performs global sensitivity analysis on synNotch-CAR T-cells to identify key engineering targets, such as ligand association rates and promoter strength, that enable the dynamic tuning of therapeutic activity for cancer treatment.
This paper presents a Course-based Undergraduate Research Experience (CURE) developed at Colorado State University that utilizes rapid, viral-based screening of guide RNA efficacy to overcome the long timescales of plant transformation, thereby enabling undergraduate students to successfully design and validate gRNAs for synthetic regulation of gene expression in *Arabidopsis thaliana*.
The AlphaFold Protein Structure Database has been expanded to include 1.8 million high-confidence protein complexes across nearly 4,800 proteomes, providing a foundational resource for understanding molecular interactions and facilitating functional discovery across biology.
This paper presents an expedient, biology-laboratory-compatible method for synthesizing functional perfluoropolyether fluorosurfactants via direct carbodiimide coupling, enabling in-house production of customizable surfactants that support diverse droplet microfluidic applications such as genomic screening, thermocycling, and protein crystallization.
Transient attenuation of actomyosin contractility reprograms confluent epithelial cells into a protrusion-driven, leader-like state by coordinating cytoskeletal, adhesive, and ERK signaling changes, thereby triggering a transition from solid-like to fluid-like tissue behavior.
This study combines automated liquid handling with an active learning framework to efficiently optimize the composition of the PURE protein synthesis system, achieving up to a 3-fold yield improvement while revealing that optimal formulations are DNA concentration-dependent and gene-specific.