196 papers
Bioengineering sits at the vibrant intersection of biology and engineering, where scientists design new tools to understand life and solve real-world problems. From creating artificial organs to developing smart materials that mimic natural tissues, this field turns complex biological questions into tangible innovations that improve human health and our environment.
At Gist.Science, we track every new preprint in this category directly from bioRxiv. Our team processes each submission to provide both plain-language explanations for curious minds and detailed technical summaries for researchers, ensuring these cutting-edge discoveries are accessible to everyone. Below are the latest papers in bioengineering that have recently appeared on bioRxiv.
This study demonstrates that utilizing sodium ion adducts combined with electron-activated dissociation tandem mass spectrometry overcomes the dehydration limitations of protonated forms to enable stable, high-confidence structural profiling and precise localization of functional groups in diverse sphingolipids.
This study utilizes in-silico Design-Build-Test-Learn cycles across four metabolic models to demonstrate that screening capacity and DNA library structure are critical drivers of optimization success, while sequencing capacity has minimal impact, thereby providing actionable guidelines for designing efficient metabolic engineering workflows.
This study demonstrates that vascular stiffness mechanically drives endothelial senescence and inflammation in both aging and doxorubicin-induced cardiotoxicity, revealing an optimal physiological stiffness window and identifying tissue mechanics as a promising therapeutic target for cardiovascular and cardio-oncological interventions.
This study demonstrates that specific extracellular matrix proteins, particularly fibrin and collagen I, significantly enhance human lymphatic endothelial cell barrier function and junctional integrity by modulating ZO-1 expression and RhoA-mediated stress fiber formation, thereby providing a foundation for improved in vitro models of lymphatic physiology and disease.
This study demonstrates that supervised machine learning, trained on a controlled dataset of over 12,000 designed variants of a membrane protein, can accurately predict expression levels in *E. coli* and guide protein engineering to achieve an 8-fold increase in purification yield.
The authors developed a high-throughput SpyCI-LAMBS assay to characterize the metal-binding selectivity of 621 lanmodulin orthologs, identifying variants capable of highly efficient one-stage separation of PrIII from LaIII to advance rare-earth element purification and metalloprotein design.