138 papers
This study develops and calibrates coupled ordinary and partial differential equation models to simulate and quantify the hormone- and cytokine-dependent proliferation dynamics of human endometrial epithelial and stromal cells in 3D co-culture, thereby elucidating their mutual interactions and the homogeneity of molecular exposure across physiological and pathological conditions.
By constructing a single-cell transcriptomic atlas of human hematopoietic stem cells to develop a stem cell aging clock, researchers identified that a biologically "younger" transcriptional age deviation in acute myeloid leukemia serves as a powerful, independent predictor of poor survival and therapy resistance, effectively refining risk stratification beyond current genetic classifications.
TRAILBLAZER is a scalable generative model that overcomes the limitations of treating cells as independent observations by utilizing a multicellular transformer architecture and a structured hyperspherical latent space to accurately predict patient-level, zero-shot responses to genetic and pharmacological perturbations while preserving single-cell resolution.
This paper presents a unified stochastic dynamical-systems and control-theoretic framework that integrates pseudotime, RNA velocity, and optimal transport to predict single-cell fate transitions, clarify identifiability from snapshot data, and recast cellular reprogramming as a probabilistic control problem for minimal-input fate shifting.
This study presents an enhanced ordinary differential equation model of the plant circadian clock and employs a multi-layered computational analysis to reveal that transcriptional repression, protein degradation, and light-regulated synthesis form a hierarchical, robust network centered on the CCA1/LHY and PRRs feedback loop.
The paper introduces mCanonicalTockySeq, a systems-level framework that integrates fluorescent timer-based signalling history with single-cell RNA sequencing to construct an experimentally anchored temporal-developmental state space, enabling the resolution of T-cell maturation dynamics and the cross-species projection of human thymic transcriptomes onto a mouse reference.
The paper introduces MICA, a model-informed change-point detection algorithm that combines binary segmentation with a genetic algorithm to identify structural transitions in simulatable dynamical systems by minimizing discrepancies between model simulations and observed data.
GeNETop is a novel methodology that integrates flux variability analysis, network topology metrics, and transcriptomic data to generate context-specific genome-scale metabolic models that remain dynamically feasible and computationally efficient for studying time-dependent metabolic processes.
By integrating interpretable machine learning with systems biology, this study overcomes linkage disequilibrium limitations to decode complex genotype-phenotype maps in yeast, successfully identifying causal genes, validating pleiotropic effects, and uncovering novel biological functions like PDR8's role in cell wall integrity.
This study presents a multiscale computational framework that integrates macro-scale interactomics, atomic-level structural modeling, and mesoscale stochastic simulations to reveal how structural constraints and spatial dynamics govern FGFR1 receptor clustering and signaling at cell-cell interfaces.
The authors have expanded TheCellMap.org from a yeast-focused repository to a comprehensive platform for visualizing and exploring a genome-scale genetic interaction network comprising approximately 89,000 interactions derived from CRISPR-based analysis of human HAP1 cells.
This paper presents a flexible framework that utilizes convolutional autoencoders to map experimental imaging data and agent-based model outputs into a shared latent space, enabling the quantitative optimization of model parameters to accurately reproduce complex spatial tumor features across diverse modalities.
BoolDog is a Python package that facilitates the construction, simulation, and analysis of Boolean and semi-quantitative biological networks by integrating discrete logic-based modeling with continuous ODE systems and supporting interoperability with standard network analysis libraries.
This study employs integrative structural modeling combining crosslinking mass spectrometry and AlphaFold to elucidate the assembly mechanisms of HDAC1/2 within NuRD, SIN3, and CoREST complexes, revealing that the intrinsically disordered C-terminal domain of HDAC1 folds into alpha helices upon complex formation.
By applying a follicle-aware spatial transcriptomics framework to human thyroid tissue, researchers reveal that epithelial heterogeneity is primarily organized by a stress-function tradeoff between active hormone-producing thyrocytes and spatially clustered damage-response cells, rather than by variations in hormone synthesis.
This study demonstrates that sex-specific physiological network robustness necessitates distinct biomarker panels for accurate biological age estimation, revealing that while older males currently offer the optimal signal-to-noise ratio for cost-effective geroscience trials, developing validated female-specific panels is an urgent priority to overcome the challenges posed by greater female physiological resilience.
This study introduces ADNeuronNet, a comprehensive, neuron-specific protein-protein interactome map derived from human iPSC-derived excitatory neurons, which reveals novel AD risk gene interactions and uncovers a previously unknown BIN1-APC/C-APOE regulatory axis that modulates Tau aggregation.
This paper presents the first thermodynamically consistent, mechanistically derived mathematical model of the mitochondrial dicarboxylate carrier SLC25A10, based on a ping-pong framework and Bayesian inference, which elucidates how mitochondrial morphology and metabolic perturbations, particularly under SDH deficiency, regulate substrate exchange and succinate handling.
This paper introduces a publicly available collection of 1,100 synthetic benchmark problems derived from 22 published models to address the scarcity of well-curated datasets, thereby enabling the systematic evaluation and optimization of computational algorithms for dynamic modeling of cellular processes.
The authors present a cost-efficient, high-throughput in vitro platform utilizing human-derived immune cells and multi-scale omics approaches to screen for and characterize personalized immune senomodulators, thereby accelerating the translation of anti-aging therapies from preclinical research to clinical application.