Gene Regulatory Network Analysis

The paper develops interval analysis methods to rigorously bound steady states in nonlinear biomolecular systems and evaluates their performance on gene network models.

The paper develops a metabolic model to test if adaptive enzyme regulation enables improved recovery after repeated perturbations and how environmental variability affects metabolic plasticity.

COCOA.jl identifies concordant and kinetic modules in biochemical networks, enabling analysis of concentration robustness with improved scalability.

This study analyzes noise in biomolecular topologies that implement derivative-action signal differentiation, showing that noise amplification can be avoided.

The paper introduces a unified SDE framework for efficient stochastic transcription trace generation, showing high accuracy with reduced computational cost.

The paper re-examines mRNA-protein correlations in E. coli, develops a theoretical framework accounting for cell growth and division, and validates it with simulations and data.

The paper introduces PEPR-GNN, a graph neural network framework to model regulome responses during genetic perturbations using Multiome Perturb-Seq data.

The paper identifies network topology principles that determine sequential drug efficacy through bistability-mediated state switching.

DoFormer improves perturbation prediction by using a causal Transformer that distinguishes observational from interventional regimes without DAG assumptions.

SETIA infers causal gene regulatory networks that reproduce transcriptional profiles as stable attractors using molecular priors and validates them on yeast RNA-seq data.

This paper introduces VCHarness, an AI system that automatically builds perturbation-response models for virtual cells, outperforming manual methods and reducing development time.

The paper identifies NT-genes as optimal targets for partial cancer reversal by analyzing gene regulatory networks and their impact on tumor and normal states.

GRNPred is a graph transformer model pretrained on gene expression data to infer gene regulatory networks, achieving high AUROC and AUPRC scores on benchmark datasets.

The paper analyzes how variability in cell division times affects protein concentration variability in cell lineages and populations, showing age-dependent differences in variance and skewness.

The paper introduces a parametric failure-based model to quantify and compare cell-state transition kinetics across conditions, showing it captures delay, timescale, hazard form, and response fraction.

This study identifies a core network structure that explains hormetic dose-response curves in drug development and applies it to rapamycin treatment.

DeSCOPE is a variational autoencoder framework for predicting genetic perturbation responses across multiple omics modalities, outperforming baselines in unseen gene and cell type scenarios.

This study models gene expression variability in single cells and growing populations with feedback regulation, finding analytical solutions for protein concentration distributions and quantifying population growth parameters.

The paper identifies an intergenerational scaling law governing stochastic cell size homeostasis in bacteria, revealing two adaptation mechanisms and tradeoffs across species and conditions.

Antimony 3 updates the human-readable modeling language to support SBML Level 3 features, enabling unified model definitions with kinetic reactions, uncertainty, annotations, and visualizations.

This study shows that mitotic oscillators trade energy for precision, with higher energy improving timing accuracy but reducing speed, and embryonic cell cycles balance speed and accuracy through metabolic compromise.

This paper presents a framework for integrating single-cell and bulk data using nonlinear mixed-effect models and tailored parameter estimation, improving parameter identifiability and prediction accuracy.

This paper proposes a framework using information entropy and evolutionary conservation to construct gene regulatory networks from sequence data, demonstrating improved edge discrimination and directionality resolution.

This paper analyzes gene regulatory networks to show how cell fate transitions and spatial pattern formation arise from temporal multistability and network topology.

The paper develops a stochastic model of gene expression with state-dependent protein activation delays, showing that such delays reduce protein level fluctuations.

This paper shows that spatial heterogeneity in minimal stochastic models can drive polarization dynamics, including stochastic switching and pole coexistence, without complex biochemical mechanisms.

This paper develops an AI framework integrating stochastic and deterministic models to simulate multiscale biological systems, enabling adaptive treatment strategies through reinforcement learning.

The paper identifies a geometric control principle in non-conservative gene regulatory networks that determines phenotypic transitions in breast cancer by analyzing phase-space distances and bifurcation structures.

BioOS is a mechanistic runtime for plant development that simulates gene regulation and morphogenesis, achieving high accuracy on multiple developmental benchmarks.

BoolDog is a Python package for Boolean and semi-quantitative network modeling, enabling simulation, analysis, and integration with continuous ODE systems.