11 papers
This paper presents an extended mechanistic model of CD4+/CD8+ CAR-T cell dynamics regulated by tumor antigen burden, demonstrating how combining sensitivity analysis with machine learning can elucidate treatment drivers and partially recover predictive accuracy from noisy patient data despite parameter uncertainty.
This paper presents an age-structured PDE model of the cell cycle to analyze how the availability of population summary data, such as FACS and FUCCI measurements, influences the identifiability of model parameters and determines the minimum data requirements for successfully fitting structured population models.
This study demonstrates that evanescent wave-based optical manipulation can non-invasively detect significant glucose-induced reductions in red blood cell mobility, suggesting its potential as a tool for probing membrane mechanical properties.
This paper proposes a phase-field framework for simulating intrinsic and extrinsic apoptotic cell death driven by an activation field, which successfully reproduces key morphological transitions like shrinkage and fragmentation while offering a potential tool for computational therapeutic testing.
This paper presents a quantitative theory explaining how the spherical cell body of "eyeless" *Chlamydomonas* mutants acts as a lens to create internal caustics, causing a phototaxis sign reversal because the flagellar response is dominated by the rapidly varying lensed signal rather than the direct illumination.
This paper introduces a novel three-stage mechanistic interpretability method that extracts a compact, high-performing hematopoietic algorithm directly from the internal attention weights of the scGPT foundation model, achieving superior zero-shot classification and pseudotime ordering on independent datasets with significantly fewer parameters and training time than standard probing or retraining approaches.
This paper demonstrates that single cells can rapidly and accurately infer the direction of a chemoattractant source at ultra-low concentrations by leveraging the disproportionately high directional information contained in early, extreme first-passage receptor binding events rather than waiting for steady-state concentration profiles.
This paper presents a multiscale mathematical model based on the kinetic theory of active particles to simulate and compare the efficacy of combined radiotherapy, chemotherapy, and anti-angiogenic therapy on glioma invasion within realistic brain geometries derived from DTI data.
This study introduces causal circuit tracing to reveal that distinct single-cell foundation models (Geneformer and scGPT) share conserved computational architectures characterized by inhibitory dominance and biological coherence, with cross-model consensus identifying disease-associated domains that are validated by CRISPRi as reflecting co-expression rather than causal encoding.
The paper proposes Cell-Mechanistic Neural Networks (Cell-MNN), an end-to-end encoder-decoder architecture that utilizes locally linearized ODEs to efficiently model single-cell differentiation dynamics and explicitly learn interpretable, biologically consistent gene interactions, outperforming current state-of-the-art methods in scalability and interpretability.
The paper proposes DCENWCNet, a novel deep CNN ensemble model that integrates three uniquely configured architectures to achieve state-of-the-art accuracy and robustness in white blood cell classification on the Rabbin-WBC dataset, while employing LIME to enhance model interpretability and trust in automated diagnosis.