138 papers
This study demonstrates that active learning-guided habitat reconstruction can achieve bacterial fitness increases equivalent to those from genome evolution, revealing that ecological adaptation offers a viable alternative to genetic mutation for overcoming evolutionary constraints.
HiMaLAYAS is a Python-based framework that enables post hoc enrichment-based annotation of hierarchically clustered matrices by treating clusters as statistical units to test and visualize significant annotations across both biological and non-biological domains.
This study demonstrates that a novel monoclonal antibody targeting CD160, which depletes NK cells and other cytotoxic T cell subsets involved in multiple inflammatory pathways, effectively prevents myocardial dysfunction and systemic inflammation in a murine model of autoimmune-induced myocarditis, suggesting a potent therapeutic strategy for inflammation-driven heart diseases.
This study introduces a Python-based systems analysis pipeline (mdsa-tools) that integrates molecular dynamics simulations with network representations and unsupervised machine learning to reveal how specific mRNA mutations at the ribosomal A-site alter conformational dynamics and induce long-range allosteric signaling to the P-site via the CAR interaction surface.
This study employs an integrative MultiOmics approach on harbour porpoises with compromised respiratory health to reveal systemic molecular signatures of immune and metabolic dysregulation across lung and muscle tissues, offering novel biomarkers for monitoring anthropogenic impacts on marine species.
This paper demonstrates that a bistable metabolic switch in *Saccharomyces cerevisiae*, which determines whether cells adopt a fermenting "arrestor" or respiring "recoverer" state, arises from the competition between mitochondrial and cytosolic ribosomes, where the relative rates of mitochondrial versus cytoplasmic protein synthesis govern the positive feedback loop required for this conserved epigenetic transition.
This study quantifies how deep uncertainty in parameter predictions and model assumptions significantly impacts the variability of tissue-specific pharmacokinetic outcomes in physiologically based pharmacokinetic (PBPK) models, revealing substantial prediction discrepancies particularly for lipophilic, protonated molecules.
This paper proposes a universal bimodal coupling optimization strategy in biological rhythms, demonstrating that physiological systems dynamically alternate between synchronized, energy-efficient states and desynchronized, function-priority states to balance metabolic costs with functional demands.
This paper introduces a bottleneck-aware framework called glycan reachability analysis that infers tissue-specific glycan biosynthetic potential from transcriptomics by quantifying pathway capacity based on the least-abundant component, thereby overcoming the limitations of binary threshold methods in capturing quantitative biological variation.
In a randomized controlled trial, a novel oral formulation of LathMized NAD+ (LNAD+) significantly increased intracellular NAD levels and metabolic flux in healthy adults without elevating circulating NAD, demonstrating effective compartment-selective delivery and a favorable safety profile.
This paper introduces a novel pragmatic model-based approach that successfully integrates heterogeneous in vitro datasets from human liver and pancreatic models to resolve study variability, unify conclusions regarding the GLP-1R agonist exenatide, and enhance the predictive utility of experimental cell systems in preclinical drug discovery.
This study reveals that human metabolic enzymes exhibit distinct amino acid archetypes unique to multicellular organisms, which are governed by a multi-layered economic system involving cost-saving, dietary trading, and the internalization of negative externalities, rather than a single principle of cost minimization.
By applying extremal graph theory to demonstrate that the prevalence of three-way junctions in mitochondrial networks statistically necessitates a large connected component, the authors propose that this common morphological pattern may arise from simple combinatorial constraints rather than requiring a specific biological explanation.
This study presents an integrated pan-cancer proteogenomic analysis of the Ubiquitin Proteasome System across 20 tissues and 10 tumor types to identify mutation-associated protein changes, clinically actionable E3 ligases, and regulatory networks, while introducing the UbiDash platform to facilitate therapeutic prioritization and mechanistic insight.
MINGL is a probabilistic framework that transforms discrete spatial-omics neighborhood annotations into continuous measures of tissue architecture, enabling the quantification of cellular gradients, interface heterogeneity, and hierarchical interactions across diverse biological contexts.
This study identifies a trajectory-coupled network bottleneck involving the CDK1-CDKN1A-WEE1 axis that governs gemcitabine resistance in 3D pancreatic cancer tissue models by enforcing an "S-phase persistence" state, a mechanism validated across a large patient atlas and applicable to broader cancer chemoresistance.
The paper introduces VIOLIN, a modular and configurable Python framework that systematically reconciles heterogeneous molecular interaction graphs extracted from scientific literature with curated baseline models by classifying relationships as corroboration, contradiction, flagged cases, or extensions, while demonstrating high stability, interpretability, and alignment with expert curation across various NLP systems and large language models.
By mapping the Gibbs free energy landscape of mouse liver glucose metabolism using a novel computational method, this study reveals that the organ maintains thermodynamic robustness against metabolic shifts caused by both fasting and obesity through substantial enzyme expression costs that buffer metabolite fluctuations to ensure efficient metabolic switching.
The paper introduces NeighborFinder, an efficient R package that infers interpretable, species-centered local microbial networks using cross-validated penalized regression, offering a targeted alternative to computationally intensive global network inference methods for large metagenomic datasets.
This paper presents a general graph-based machine-learning approach that accurately predicts the effects of gene perturbations on various molecular phenotypes, offering a cost-effective alternative to exhaustive experimental screening by leveraging existing data to prioritize targets, hypothesize mechanisms, and generalize across unmeasured genes and phenotypes.