Gene Regulatory Network Analysis

The paper identifies division asymmetry as the main driver of cell size variability in budding yeast, showing it outweighs G1/S size control effects.

QWENDY infers gene regulatory networks from single-cell gene expression data across four time points, outperforming existing methods on experimental data but not synthetic data.

The paper introduces a method to identify indicator molecules in biochemical reaction networks that can distinguish between multiple steady states without kinetic parameters.

The paper examines how JNK activation dynamics influence gene expression patterns through mRNA stability and pathway-specific regulation.

The paper introduces TFA (Transcription Factor Activity) to infer TF-TF interactions with high fidelity, outperforming expression-based methods and revealing isoform-level heterogeneity in regulatory circuits.

This paper develops a computational pipeline to infer kinetic parameters from single-cell RNA sequencing data and assesses parameter identifiability using the stochastic telegraph model.

The paper analyzes monotone Boolean network models of gene regulatory networks to identify common steady states and multistability patterns, showing epithelial and mesenchymal states are most common in the EMT network.

The paper introduces ctsGRN, a framework for inferring cell type-specific gene regulatory networks in Arabidopsis roots using multiple computational methods, and identifies key transcription factors and regulatory modules.

This paper develops a hybrid neural-symbolic framework to uncover the mathematical principles linking cellular energetics to genetic regulation using simulated time-series data.

scTFBridge infers gene regulatory networks from single-cell multi-omics data by integrating TF-motif binding knowledge, outperforming baselines in cis- and trans-regulation tasks.

The paper develops a data-informed model of AMPK signaling using uncertainty quantification and biosensors, showing AMPK integrates inputs over time during exercise.

RepliFlow infers cell cycle dynamics from flow cytometry data, capturing changes in DNA replication program alterations.

The paper develops a mathematical framework showing intercellular signaling stabilizes cell fate and tissue patterning by suppressing spontaneous switching and inducing coordinated behaviors.

The paper shows that molecular signaling networks can resist temperature and ionic strength changes through topological compensation using compensatory reactions.

The paper develops a framework to integrate multiplexed immunofluorescence data with mathematical models to study cell cycle dynamics and arrest, validated on MCF-10A cells exposed to arresting agents.

The paper shows that multi-team competition in gene regulatory networks leads to less stable outcomes compared to binary competition, explaining why binary decisions are common in biology.

The paper analyzes gene regulatory networks across 9,000 tumors to identify recurrent PD-1 pathway dysregulation linked to patient outcomes.

The paper introduces a framework to analyze gene regulatory networks by identifying mutually inhibiting teams of nodes and shows how network topology predicts dynamic behavior.

The paper introduces locaTE, a method to infer cell-specific gene regulatory networks using single-cell dynamics and geometry, showing improved performance over existing methods.

This paper designs and evaluates minimal genetic circuits that enable cells to anticipate environmental trends through deterministic and stochastic analyses.

This paper engineers synthetic epigenetic circuits that induce stable gene expression changes and drive cell state transitions through transient cell-cell interactions.

The paper introduces GRN_modeler, a user-friendly tool for constructing and evaluating gene regulatory networks, demonstrating its application in designing oscillators and a light biosensor in E. coli.

This paper introduces RiCE, a physics-based algorithm for inferring gene networks from time series data, showing improved performance across multiple datasets and applications.

The paper introduces BoNesis, a tool for inferring Boolean networks from transcriptome data to model cellular differentiation and reprogramming, demonstrating its application in hematopoiesis and adipocyte/osteoblast differentiation.

PyRID is a Python-based reaction-diffusion simulator for molecular biological systems, capable of simulating reactions and interactions between proteins and substrates.

This study uses biophysical models to show that transcription factors can act as activators, repressors, or both, with their effects influenced by binding affinity and regulatory context.

The paper demonstrates that the Fano factor can be used to determine cooperativity in biological networks, showing that non-linear Fano factor variation indicates positive cooperativity.

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

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.