Gene Regulatory Network Analysis

The paper investigates the relationship between binding statistics and Shannon information in cooperative biological networks using two models.

This paper designs sigma factor-based biomolecular classifiers and investigates how resource competition affects their performance and design principles.

This paper introduces an optogenetic clock system that can synchronize populations of engineered cells using light inputs.

The paper examines how the BMP-Smad pathway balances trade-offs among response speed, noise amplification, and sensitivity to receptor input by tuning non-conserved parameters.

The paper models chemical reaction networks in a random environment using Hawkes processes to approximate marginal descriptions of the system.

This paper introduces a perturbation method to refine Boolean models of cell cycle regulation in budding yeast and mammalian cells by optimizing network dynamics.

This paper introduces a novel numerical simulation method for biochemical reaction systems and demonstrates its application to various reaction types.

The paper develops a dynamical model of the cell cycle as two mutually inhibitory oscillators, showing how this framework can explain various cell cycle behaviors.

COFFEE improves gene regulatory network inference in scRNA-seq by integrating multiple methods through a consensus approach, outperforming individual algorithms on various datasets.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

This study creates a synthetic genetic toggle switch that exhibits population bistability and generates spatial differentiation patterns in bacterial colonies.

This paper introduces a framework to infer gene regulatory networks (GRNs) during tissue morphogenesis by integrating cell movement and gene expression data.

The paper identifies the core mechanism for stochastic intergenerational cell size homeostasis in bacteria using high-precision data and first passage process analysis.

The paper develops a mathematical model to show that fitness maximization leads to protein overabundance, a transcriptional floor, and balanced transcription-translation load.

WENDY infers gene regulatory networks from single-cell gene expression data with time-course interventions by modeling covariance dynamics, showing good performance on synthetic and experimental data.

This study introduces a mathematical model for Molecular Competition Networks and shows they outperform non-competitive networks in computational tasks.

The paper models relaxation experiments using a mechanistic gene expression model with a proliferation term, showing good agreement with experimental data on CD34 antigen distribution.

This study shows that cell signaling systems exhibit transient frequency preference responses during early-stage stimulation, which are absent in periodic regimes and may influence higher-order circuits.

This study shows low-affinity binders can modulate high-affinity interactions through herd regulation, impacting biological processes like sex determination and signaling thresholds.

This study shows nonspecifically DNA-bound RNA polymerases in bacteria buffer free RNAP concentration, reducing crosstalk between rRNA and mRNA transcription, and are evolutionarily advantageous.

The paper builds a mathematical model of the budding yeast START transition, integrating known molecular interactions and phenotypes to replicate key mutant behaviors and dynamics.

Holimap approximates gene network dynamics using a simpler reaction system, showing computational advantages over conventional methods in predicting stochastic time-dependent dynamics.

This study uses a stochastic model to analyze time-resolved single-cell RNA-sequencing data and identifies global transcription regulation mechanisms through dynamical correlations.

The paper uses model-X knockoffs to assess false positives in regulatory network inference and identifies causal sufficiency as a major limitation.

Poincare and SimBio are Python packages for CRN modeling and simulation, offering extensibility and performance through Numba compilation and SBML support.

The paper introduces the BioRECIPE format for representing biological models to enable human-machine interaction and model development.

Ligify predicts transcription factors responsive to user-defined chemicals and generates plasmids for biosensor development, achieving 31/100 correct predictions in benchmarking.

The paper introduces a Design of Experiment strategy to refine Gene Regulatory Networks by selecting informative perturbations and validating predictions against experimental data.

STREAM infers enhancer-driven gene regulatory networks from single-cell RNA-seq and ATAC-seq data, showing improved performance in TF recovery and enhancer-gene discovery.

This paper introduces a method to link scRNA-Seq data with Boolean gene activation states, enabling both binarisation of real data and generation of synthetic data for model validation.