138 papers
This paper demonstrates that despite the vast number of possible gene network topologies, only three fundamental classes and their combinations are capable of driving pattern formation through extracellular signaling, providing a unified logical framework that encompasses both known and potential biological mechanisms.
By applying a cross-cohort validated machine learning approach to single-nucleus RNA sequencing data, this study identifies microglial PTPRG as a central transcriptional hub that integrates neuronal signaling and inflammatory dysregulation in Alzheimer's disease.
This study demonstrates that genes linking circadian rhythm regulation and behavioral plasticity are broadly conserved across ant species by identifying significant overlaps between rhythmic gene expression modules and foraging-related plasticity genes in both harvester and carpenter ants.
This paper presents a nonlinear mixed-effect modeling framework with a tailored parameter estimation scheme that successfully integrates diverse single-cell and bulk data types to improve parameter identifiability and prediction accuracy in complex biological processes, as demonstrated through the analysis of extrinsic apoptosis.
This paper introduces the stochastic Boltzmann Machine (sBM), a novel regularization strategy that more accurately captures multiscale statistical correlations in protein sequences, thereby enabling generative models to produce functional and diverse proteins without requiring post hoc corrections.
Using molecular dynamics simulations, this study reveals that the highly conserved CAR motif structurally and functionally interacts with the ribosomal A site through sequence-dependent hydrogen bonding and pi-stacking, suggesting a mechanism for mRNA sequence-specific tuning of translation elongation.
This study combines transcriptomic analysis and computational modeling to demonstrate that time-dependent hypoxic memory, which allows cancer cells to retain invasive phenotypes after reoxygenation based on prior hypoxia duration, significantly enhances tumor invasion by enriching hypoxic cell density at the tumor front, reducing sensitivity to oxygen fluctuations, and increasing effective cell diffusion.
This study utilizes UK Biobank clinical data to develop a domain-specific "IC age" mortality risk estimator and establishes immune-related proteomic surrogates to enable scalable monitoring of functional health decline.
This paper introduces UQ-PhysiCell, an open-source, extensible Python framework that streamlines uncertainty quantification, calibration, and model selection for PhysiCell agent-based models by providing a scalable, parallelized workflow that integrates with established statistical analysis libraries.
This study demonstrates that integrating proteomic kinome profiling with machine learning enables the prediction of kinase inhibitor cardiotoxicity by identifying specific off-target kinase engagements, such as PDGFRB and EGFR, that drive adverse cardiac events.
This paper presents an AI-assisted framework that integrates large language models with SBML-based modeling to automate the reconstruction, extension, and calibration of Quantitative Systems Pharmacology models, demonstrated through the successful development of a credible CAR-T therapy model that maintains mechanistic transparency and regulatory alignment.
This study integrates multi-cohort transcriptomics and network pharmacology to identify CD44 as a key hub gene in PCOS granulosa cells and prioritizes specific gene-drug pairs, such as GJA5 with flufenamic acid, as promising candidates for future therapeutic development.
This study utilizes whole blood transcriptome analysis of pigs to characterize the distinct temporal dynamics of heat acclimation, revealing a biphasic response where short-term adaptation is driven by immune activation and heat shock proteins, while long-term acclimation involves complex metabolic shifts in lipid metabolism and oxidative phosphorylation to restore homeothermy.
This paper presents a unified analytical framework demonstrating that a single dimensionless parameter governs temporal cell fate transitions in symmetric gene regulatory networks, while revealing that only three-node repressive topologies, unlike two-node circuits, can support stable spatial pattern formation through diffusion.
This paper presents a stochastic model demonstrating that state-dependent activation delays, combined with negative feedback, can effectively reduce protein expression fluctuations below baseline levels, thereby enhancing cellular stability and predictability.
This study establishes a resource competition model that disentangles the fitness cost of gene expression into distinct components driven by ribosome, RNA polymerase, and transcription factor limitations, revealing that these costs originate from the transcription and translation processes themselves rather than the gene products, thereby providing a quantitative framework to optimize genetic design in synthetic biology.
This study extends a computational model of quadrupedal locomotion to demonstrate that animals utilize distinct, speed-dependent asymmetrical turning strategies—specifically body bending at low speeds, lateral force application at medium speeds, and lateral limb shifting at high speeds—with forelimbs primarily driving steering while hindlimbs modulate propulsion and stability.
This study introduces DIGEST, an ultrasensitive digital immunoassay capable of quantifying fecal host proteins at attomolar levels, enabling non-invasive tracking of gut immune dynamics for distinguishing inflammatory bowel disease states and predicting immunotherapy responses in melanoma.
This study identifies phosphorylated PAGE4 as a critical downstream effector of MED12 mutations in uterine leiomyomas, where HIPK2-mediated phosphorylation acts as a molecular switch to restructure PAGE4's interactome and drive tumor pathogenesis through altered transcriptional machinery recruitment.
This paper introduces sRQA, an open-source R package that extends Recurrence Quantification Analysis to discrete state sequences, demonstrating its utility across cardiac, neural, and speech data to reveal meaningful dynamical patterns that are often inaccessible to traditional continuous-signal methods.