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This paper integrates GLP-1RA exenatide studies across heterogeneous cell systems to address variability, providing a unified conclusion and predicting inter-study variabilities.

The paper introduces a physics-constrained neural ordinary differential equation model to predict microbial community dynamics and interactions, outperforming existing mechanistic and machine learning approaches.

The paper uses molecular dynamics simulations to study the interaction between the ribosome's A site and CAR site, revealing sequence-dependent modulation of hydrogen bonding and π-stacking interactions.

The paper identifies metabolomic signatures associated with extreme old age using data from the New England Centenarian Study and links them to biological age and mortality risk.

The paper identifies a CDK1-CDKN1A-WEE1 bottleneck in gemcitabine resistance using trajectory-coupled network analysis in PDAC models and patient data.

MINGL quantifies tissue organization by modeling cell memberships across hierarchical units, detecting interfaces, gradients, and heterogeneity in spatial-omic datasets.

BARMIX quantifies treatment resistance in gastrointestinal stromal tumor cell mixtures using barcoded experiments and probabilistic modeling, accurately reflecting clinical resistance patterns.

This paper evaluates proteomics workflows for cultivated meat, showing device-based and optimized protocols improve proteome coverage and standardization.

This study identifies a strategy for improving flight efficiency in insects by analyzing how distributed elasticity in flight motors affects resonance bands.

The paper develops a general method to derive expressions for Emax and EC50 in T-cell activation models and applies it to models lacking these expressions.

This paper uses agent-based modeling to simulate IPF progression and evaluate treatment efficacy by analyzing collagen accumulation and invasion under different treatment scenarios.

This paper introduces a graph-based machine learning approach to predict gene perturbation effects on molecular phenotypes using existing experimental data and biological networks.

The paper identifies thermodynamic limitations in engineered microbial strains that prevent high-titer ethanol production by analyzing metabolite profiles and thermodynamic driving forces.

This paper investigates the consistency of likelihood ratio tests in pharmacometrics by examining the continuity of likelihood functions under parameter fixation in population models.

The paper uses chemotypes derived from FTIR spectroscopy and machine learning to predict biological variation and stress response in Drosophila melanogaster.

The paper integrates multi-omics data from 101 individuals over two years to identify stable immune patterns and genetic associations with immune cell types and functions.

The paper provides a quantitative design guideline for positive feedback in biomolecular systems, showing how feedback strength relates to saturation parameters and demonstrating its application in gene regulation models.

The paper develops a cross-species translation framework to identify therapeutic targets for late-onset Alzheimer's disease using mouse models and human data.

GRNFormer infers gene regulatory networks from transcriptomics data using a graph transformer framework, outperforming existing methods and recovering known and novel regulatory interactions.

The paper integrates metagenome-scale metabolic modeling and metabolomics to analyze Microcystis phycospheres, revealing metabolic interactions and functional differences among strains.