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MetaNetMap automates mapping of metabolomic annotations onto genome-scale metabolic networks through direct, conversion-table-bridged and partial identifier matching, reproducing 311 mappings from a Parkinson's disease study.

This study defines the physiological limits of efflux-mediated drug resistance in mycobacteria through adaptive evolution experiments, revealing a resistance ceiling and metabolic rewiring as key factors.

This paper constructs a gene regulatory network for Aspergillus fumigatus using RNA-seq data, validates it with experimental cases, and provides an online resource called GRAsp.

This study models the impact of control measures on HPAI H5N1 transmission in poultry in Great Britain, finding that enhanced biosecurity and vaccination can reduce infected premises.

The paper identifies aging-associated alternative splicing programs in human and mouse tissues using scRNA-seq and introduces LeafletFA for discovering coordinated splicing programs.

The paper quantifies how growth rate and protein production fluctuations contribute to gene expression noise in E. coli, finding growth rate fluctuations are largely independent and decrease with mean growth rate.

The paper uses high-resolution microCT to create a 3D digital octopus, revealing neural pathways and organ structures from periphery to brain.

This paper introduces a hybrid calibration framework that integrates expert knowledge into multi-objective optimization for biological model parameter estimation, showing improved biological relevance and reduced overfitting.

The paper introduces 1100 synthetic benchmark problems for dynamic cellular process modeling to improve algorithm evaluation.

The paper uses AI and causal genetics to identify shared therapeutic targets for aging and 12 age-related diseases across four disease areas.

The paper improves genome-scale flux profiling in human cell metabolism by introducing a new method for exo-metabolite exchange flux calculation, enhancing the biological relevance of metabolic models.

This paper introduces SPER, a method to identify paracrine regulators of cell type composition from spatial transcriptomics data, showing it detects genes involved in extracellular secretion and receptor-ligand interactions.

This paper introduces DIP-DCM, a method that improves parameter estimation in neural mass models by using dynamics-informed priors, showing better performance in predicting neuroimaging data and capturing disease mechanisms.

This study uses metabolomics to analyze how two CLOs disrupt MRSA membranes, revealing distinct metabolic changes that indicate membrane destabilization and lipid remodeling.

This paper uses mathematical modeling to show adaptive therapy's success depends on treatment thresholds and competitive interactions, with no optimal strategy due to impractical infinite treatment holidays.

This study extends a gene pair classification method to ordinal outcomes, showing comparable performance with higher interpretability in glioblastoma and ovarian cancer.

This study develops a genome-scale metabolic model of Flavonifractor plautii to explore its probiotic potential and ecological roles in the gut microbiota.

This paper extends integrated information theory to identify intrinsic units by assessing cause-effect power at different grains in simulated systems.

This paper introduces an unsupervised domain adaptation method for cell segmentation that avoids predefined thresholds, achieving accuracy comparable to supervised models without labeled data.

This study shows that orchard management practices alter citrus root and rhizosphere microbiomes, which in turn affect plant performance through microbial community changes.