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The paper shows that second messengers in E. coli affect gene expression through transcriptional competition, reshaping proteome allocation beyond individual regulatory effects.

This study shows that MNS reduces viral infection and inflammation in cell and animal models by suppressing inflammatory pathways and altering chromatin accessibility.

The paper compares kinetic models of E. coli ammonium transport and assimilation to determine the mechanism of energy coupling and growth robustness under varying conditions.

This paper presents a Bayesian framework for parameter inference in ODE models that accounts for uncertain measurement times, improving reliability and efficiency in dynamic modeling.

This paper identifies a bifurcation state in Parkinson's disease transcriptomics using a Riemannian tensor framework, showing it as a geometric saddle point between healthy and diseased states.

The paper presents a systems-level analysis of the endocannabinoid system (ECS) using network integration, identifying hubs, communities, and resilience mechanisms.

This study shows that myeloid differentiation in hematopoietic progenitors starts during S phase and is linked to chromatin changes and HOXA9 activity.

This paper introduces MCI, a method to estimate absolute counts from proportional data using multivariate normal distributions, and demonstrates its effectiveness on white blood cell counts and single-cell mRNA data.

This paper develops an uncertainty-aware framework for PAD biomarker discovery using graph neural networks and positive-unlabeled learning, identifying novel and validated candidate proteins.

The paper characterizes gene regulatory networks in human airway epithelial cells and identifies cell type-specific responses to type I interferon.

This paper introduces DySCoMeMo, a framework that combines dynamical systems and metabolic models to forecast microbiome-host dynamics from ecological data, achieving accurate predictions without requiring direct metabolite measurements.

The paper integrates thirteen fungal metabolic models into a unified framework, improves their consistency, and uses them to analyze bioenergetic differences across species.

The paper identifies age-related miRNA deregulation patterns in aging rhesus macaques, highlighting tissue and sex-specific changes and cross-species conservation.

TRAFIKK predicts and explains anticancer drug synergies by simulating drug effects in cell-specific models, achieving high recall and revealing both conserved and context-dependent mechanisms.

This study identifies host-derived PAFAH as a schistosomicidal phospholipase that remodels diacylglycerophospholipids, impairing schistosome development and survival.

This study uses genome-scale metabolic modeling and 13C-based pathway mapping to identify key enzymes for enhancing camptothecin production in the endophyte Alternaria burnsii.

This study shows Drosophila larvae adjust foraging behavior based on resource quality, valence, and prior experience, using a simple integration model.

This study uses spatial transcriptomic and proteomic profiling to identify cell interaction patterns that predict progression of Barrett's esophagus.

The paper uses proteomic kinome profiling and machine learning to predict kinase inhibitor cardiotoxicity by analyzing off-target kinase engagement and its association with cardiac adverse events.