Gene Regulatory Network Analysis

This paper introduces an extended antithetic control motif stabilized by molecular buffering to achieve robust perfect adaptation in synthetic biology circuits.

The paper constructs a transcriptional regulatory network for Escherichia coli using machine learning on a large RNA-seq compendium, identifying regulatory modules and novel regulons.

The paper introduces a method to accurately reconstruct dynamic gene expression and growth rate profiles from noisy measurements, showing it is more accurate than existing approaches.

The paper proposes and computationally validates a method to mimic artificial neural networks using genetic circuits, demonstrating functionality in regression and classification analyses.

This paper analyzes ODE-based biochemical models with singular Jacobians, deriving an adaptation condition applicable to both single and multiple input-output systems.

BMSS2 is a Python package for model selection and identifiability analysis in synthetic biology, offering tools for simulation, parameter inference, and sensitivity analysis through a database-driven framework.

The paper identifies gene regulatory networks driving macrophage polarization using bulk and single-cell RNA-seq and ATAC-seq data from HL-60-derived macrophages.

This study quantifies the maximum information transfer across a promoter using epigenome editing and shows that chromatin state can tune gene expression and transfer functions.

The paper analyzes how noise affects the shape of potential landscapes in bifurcation systems, showing its impact on cell fate decision dynamics.

scMomentum infers cell-type-specific regulatory networks and energy landscapes from single-cell transcriptomic data without needing temporal or perturbation experiments.

The paper designs and analyzes genetic circuits for whole-cell biosensors to detect groundwater contaminants using computational modeling and simulation.

PySCNet is a tool for reconstructing and analyzing gene regulatory networks from single-cell RNA-seq data, offering methods for gene co-expression module detection and gene importance evaluation.

This study uses immunohistochemistry images and convolutional neural networks to infer gene regulatory networks in human placenta, revealing new regulatory relationships not detected by traditional methods.

The paper analyzes genetic circuits for dosage control in microbial populations using the Lux quorum sensing system and demonstrates the effects of negative feedback and degradase activity on circuit robustness.

This paper uses neural networks to approximate non-Markov stochastic gene expression models and infer their parameters from noisy data.

The paper uses dynamic network analysis to study how human fibroblasts respond to physical and biochemical cues, identifying key transcription factors involved in mechanotransduction pathways.

This paper introduces synthetic integral and proportional-integral feedback controllers in mammalian cells to achieve robust perfect adaptation, showing improved stability and reduced variability.

The paper demonstrates that complex gene expression patterns can be achieved in bacteriophage genomes by adjusting promoter, terminator, and RNase cleavage site placements and strengths without dedicated regulatory genes.

The paper develops a two-state model of gene expression that captures chemical reaction dynamics with better accuracy and robustness than traditional models.

The paper presents a model explaining how resource allocation affects the roles of promoter and RBS strengths in protein expression and cell growth in E. coli.

The paper demonstrates that a bell-shaped dose response can occur in a system without incoherent feedforward loops (IFFLs) and analyzes conditions for this in two-dimensional systems.

LoopDetect identifies feedback loops in ODE models across MATLAB, Python, and R, supporting parameter ranges and user-defined states.

This paper introduces DISCO, a method to infer single-cell signaling dynamics from snapshot data, revealing cell-to-cell variation in MAPK/ERK signaling and its dependence on initial states.

The paper develops a bottom-up model of yeast polarity genotype-phenotype map to predict evolutionary relevant phenotypes by integrating mechanistic and noisy data.

The paper develops a generalized method to detect switch-like bistability in chemical reaction networks with conservation laws, validated on known bistable motifs.

This paper introduces a constraint-based algorithm for simulating large reaction systems, showing it outperforms existing methods in computational efficiency for high molecule and reaction channel counts.

Flapjack is a tool for managing and analyzing genetic circuit data to support the DBTL cycle by organizing measurements and metadata for model parameterization and prediction.

The paper introduces cayenne, a Python package for stochastic simulations, and benchmarks its accuracy against other libraries, finding it offers the best balance of accuracy and speed.