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This study develops a mathematical model of ATP metabolism in chondrocytes to show that chronic hypoxia shifts metabolism to glycolysis, reducing ATP and causing ECM loss, with therapeutic success requiring metabolic and structural interventions.

The paper develops a framework to analyze thermodynamic models of transcriptional regulation using experimental data from Drosophila embryos.

The paper develops a computational model to analyze how MYCN-induced expression changes affect energy metabolism in neuroblastoma, revealing Warburg-like flux alterations and potential metabolic bistability.

This study investigates how adding mass to different lower-limb segments affects peak knee contact forces during walking, finding that foot mass increases forces more than thigh or shank mass.

The paper develops a high-throughput method to profile SARS-CoV-2 RBD variants' affinity against antibodies, generating binding energy landscapes for variant analysis.

The paper uses single-cell transcriptomics and neural network classifiers to identify B cell signaling as critical for NK cell expansion in allogenic therapy.

This paper introduces ObjFind-M, an inverse optimization framework that infers reaction-level metabolic objectives from fluxomic and metabolomic data in mammalian cells, finding mitochondrial ATP synthase as a central driver.

The paper evaluates the translatability of two mouse osteoarthritis models to human transcriptomics data using TransComp-R, finding that ACLR better predicts human OA conditions.

The paper introduces UCL-MetIsoLib, a high-resolution metabolomics library for isomer-resolved profiling, validated across multiple biological matrices and applied to colorectal cancer organoids.

SBMLNetwork provides a framework for standards-based visualization of biochemical models by automating the generation of SBML-compliant visualization data.

microbetag enhances microbial network interpretation by annotating nodes with phenotypic traits and edges with metabolic complementarities, using a database of annotated genomes.

scPortrait integrates single-cell images with proteomics and transcriptomics data to identify cancer-associated macrophage subpopulations and embed images into transcriptome atlases.

This study uses ultrasensitive proteomics to uncover nociceptor diversity and identify novel pain targets by analyzing protein expression in sensory neurons.

This study develops a multicellular agent-based model to simulate nanoparticle interactions in cancer therapy and evaluates how pharmacokinetic parameters and therapeutic strategies affect tumor dynamics.

The paper identifies a noise-driven differentiation switch mechanism that controls cell type diversity in neural progenitor cells through combinatorial signaling inputs.

This paper introduces in-CAHOOTTS, a neural ODE framework for inferring gene regulatory networks from dynamic single-cell data, showing it can predict gene expression and identify regulators in yeast responses to rapamycin and cell cycle progression.

This paper develops a deep learning model called SETComp to predict intervention effects of complex systems using transfer learning and permutation-invariance, achieving significant accuracy improvements over baselines.

The paper shows that reducing RNA polymerase II levels disrupts transcriptional homeostasis through organized gene expression changes and feedback loops.

The paper uses a computational model to predict how T cell exhaustion affects viral control in children with perinatal HIV, finding that exhausted CD8+ T cells can contribute to short-term viral suppression.

This paper develops an individual-based model using explainable AI to demonstrate mechanisms of direct and indirect competition between species, showing how weaker species can outcompete stronger ones and how complete competitors can coexist.