138 papers
This study utilizes an agent-based model to simulate idiopathic pulmonary fibrosis progression and evaluate the efficacy of pirfenidone and pentoxifylline treatments, both individually and in combination, by analyzing collagen accumulation and invasion across 180 in silico experiments.
This study demonstrates that FTIR spectroscopy-derived chemotypes in *Drosophila melanogaster* serve as integrative proxies for biological variation driven by sex, genotype, nutrition, and age, successfully predicting population-level differences in stress responses.
This study addresses the lack of analytical standardization in cultivated meat proteomics by comparing various workflows and demonstrating that optimized, cost-effective protocols combined with data-independent acquisition can achieve high proteome coverage, thereby establishing a standardized framework for reliable multi-omics characterization and regulatory compliance.
This paper introduces BARMIX, a scalable platform combining DNA-barcoded cell mixtures with a probabilistic framework to efficiently quantify genotype-specific treatment resistance in gastrointestinal stromal tumors, thereby enabling the systematic development of precision oncology therapies.
This paper introduces numerical methods to identify resonance bands in insect flight motors, revealing that distributing elasticity across the thorax and wings is a high-reward, moderate-risk strategy that can significantly expand the range of efficient flight states when properly tuned.
This study presents the first pangenome-based genome-scale metabolic model for *Xylella fastidiosa*, which not only guides the development of defined culture media but also predicts and experimentally validates the production of polyamines as a potential novel virulence factor.
GlycoDiveR is an open-source, modular R framework designed to streamline the analysis and visualization of high-dimensional glycoproteomics data by addressing unique glycoform complexities, integrating diverse search engine outputs, and providing accessible, customizable tools to lower the barrier for biological interpretation.
By integrating whole-genome sequencing with longitudinal multi-omics profiling in 101 healthy individuals over two years, this study reveals reproducible individualized immune patterns, identifies three distinct immunotypes with specific metabolic and inflammatory signatures, and uncovers genetic associations linking polygenic scores to memory B cell abundance and activity.
This study introduces the TransComp-R computational framework to bridge transcriptomic gaps between mouse models and human late-onset Alzheimer's disease, successfully identifying and validating the orexin receptor antagonist suvorexant as a repurposable drug that reduces phosphorylated tau levels in human patients.
This study combines computational modeling and empirical genomic data to demonstrate that developmental system drift, characterized by the continual rewiring of gene regulatory networks and turnover of conserved non-coding sequences without altering phenotypic outcomes, is a pervasive mechanism in the evolution of complex plant developmental systems.
By integrating metagenome-scale metabolic modeling with metabolomics, this study characterizes the functional dynamics of Microcystis phycospheres, revealing that while bacterial communities expand metabolic potential and maintain redundancy independent of cyanobacterial phylogeny, the overall metabolomic profile is primarily driven by the cyanobacteria themselves, thereby highlighting the ecological significance of interspecies metabolic interactions in harmful blooms.
This study utilizes a mathematical model to demonstrate that platelet-released polyphosphates enhance thrombin generation in tissue factor-initiated coagulation under flow by accelerating Factor V and Factor XI activation, thereby lowering the threshold for clotting, reducing lag time, and diminishing sensitivity to TFPI inhibition.
FASTERCC is a new, significantly faster algorithm that accelerates flux consistency testing and context-specific metabolic network reconstruction by leveraging structural information to pre-process and clean large-scale models, thereby reducing computation times by up to 20-fold compared to its predecessor FASTCC.
This study demonstrates that a multi-omic signature combining specific circulating miRNAs and proteins identified at presentation can accurately predict divergent clinical outcomes in pediatric dilated cardiomyopathy, offering a promising tool for early risk stratification and insight into underlying disease mechanisms.
By leveraging the largest genome-wide CRISPRi Perturb-seq compendium (X-Atlas/Pisces) to train a 4.9-billion-parameter diffusion language model (X-Cell-Ultra), the authors demonstrate that scaling causal perturbation data and model capacity enables zero-shot, generalizable prediction of cellular responses across diverse biological contexts, significantly outperforming existing methods.
This study employs stochastic optimal control simulations with a detailed 9-degree-of-freedom, 18-muscle model to demonstrate that while sensorimotor noise has a minimal impact on mean walking kinematics, it significantly shapes movement variability and effort, revealing that the human control strategy prioritizes stability of the center of mass and foot clearance over joint angles to minimize effort under uncertainty.
This study presents a proof-of-principle multi-physics computational framework that couples three-dimensional fluid-structure interaction simulations with a systems biology model to demonstrate how hemodynamic forces and biochemical signaling interact to drive the progression of aortic valve calcification.
This study integrates a mechanistic computational model with in vivo data to demonstrate that tumor adaptation to the bone microenvironment shifts dependence from bone-derived growth factors to autonomous proliferation, rendering osteoclast-targeted therapies effective only against non-adapted tumors while failing to control bone-adapted variants.
This longitudinal multi-omic study demonstrates that diverse high-fat diets induce persistent, source-specific alterations in the host-microbiome interactome that are only partially reversible upon dietary switching, driven by baseline microbiome composition and resulting in a lasting "microbiome memory" characterized by sustained taxonomic shifts and suppressed host immune gene expression.
This paper introduces BIRDkiss, a new open-source R-based modeling framework that integrates simplified energy budget and toxicokinetic-toxicodynamic principles to predict how pesticides and food availability interact to affect bird growth and reproduction, thereby enhancing the ecological relevance of environmental risk assessments.