Gene Regulatory Network Analysis

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.

The paper integrates mechanistic modeling with machine learning to accelerate the discovery of design rules for pattern formation in living cells.

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

The paper introduces Evolverator, a yeast system for continuous in vivo protein evolution, and demonstrates its use in improving ligand affinity and generating stronger repressors.

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.

The paper investigates how gene length affects kinetic parameters like burst size and their impact on mean expression, suggesting implications for drug target selection.

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 uses a stepwise Bayesian framework to identify a novel gene regulatory network component in neural tube development using whole-genome sequencing data from neural precursor cells treated with Sonic Hedgehog.

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

The paper develops a machine learning approach to reconstruct complex biological systems from partial faceted datasets, showing successful reconstruction in both mechanical and biological examples.

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.

CauFinder identifies and steers cell-state and phenotype transitions by causal disentanglement learning, outperforming existing methods in simulated and real-world scenarios.

The paper investigates protein overabundance in Acinetobacter baylyi by analyzing proliferation dynamics of essential-gene knockouts, finding that most essential proteins are overabundant and that overabundance increases as expression levels decrease.

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.

The paper introduces HOSS, a hierarchical optimization framework for biochemical networks, which improves model efficiency and reusability by breaking down complex systems into modular, progressively optimized components.

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.

This study identifies optimal protein expression strategies for bacterial survival under antibiotic stress, revealing hysteresis and a noise floor in gene expression.

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.