281 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.
Ligify 2.0 is an interactive web server that expands the Ligify bioinformatic method to predict over 13,000 transcription factor-small molecule interactions, providing comprehensive visualization tools, plasmid design capabilities, and experimentally validated biosensor candidates for pathogenic bacteria to facilitate the development of chemical-controlled biological systems.
This study demonstrates that a 1D U-Net deep learning model can accurately estimate sagittal plane ankle and knee joint angles from surface electromyography signals in typically developing children, offering a portable and non-invasive alternative to traditional motion capture systems for pediatric gait assessment.
This paper introduces an efficient, few-shot directed evolution method that adapts large-scale pre-trained protein language models using a statistical physics-based post-training algorithm to generate diverse, high-fitness protein sequences from sparse experimental rankings while providing interpretable insights into their biophysical properties.
Mechanistic simulations reveal that the brain's microvascular network organization and hemodynamic perfusion provide intrinsic oxygen transport reserves that protect cortical tissue from hypoxia, although age-related declines in arterial saturation and capillary flow stalling can compromise this resilience.
By screening over 1,200 natural intracellular domains in CAR T-cells, this study reveals that immunophenotype differentiation is determined not by individual signaling motifs but by the integrated combinatorial output of multiple motifs within costimulatory domains, providing a new framework for rational CAR engineering.
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.