Gene Regulatory Network Analysis

The paper develops RENIN, a regularized regression method, to predict gene regulatory networks from single nucleus multiomic data and applies it to identify regulators of failed kidney injury response.

The paper models yeast polarity site movement by incorporating calcium-mediated GAP activity spikes into an existing positive feedback model, simulating oscillating and wandering polarity sites.

The paper designs and models phase-based genetic logic gates and circuits, demonstrating their feasibility and robustness to molecular noise.

This paper introduces TIGER, a method that infers transcription factor regulomes and activities from RNA-seq data, showing improved accuracy in yeast and cancer cell lines and potential for single-cell analysis.

The paper uses dynamical modeling to show how different network motifs process oscillatory signals, identifying motifs that suppress or convert oscillations into sustained outputs.

This paper introduces a framework and tool to derive analytic steady states of complex biochemical reaction networks by breaking them into subnetworks and using a software package called COMPILES.

The paper develops a Bayesian MCMC method to infer gene regulatory network parameters from partial observations, demonstrating its ability to estimate delays and kinetic parameters in a two-step activation model.

The paper uses flux analysis to quantify gene expression dynamics in mouse bone marrow-derived DCs, revealing six regulatory modalities and highlighting protein degradation's role in LPS response.

The paper identifies two groups of strong quiescence-depth modulators using theoretical cell models and machine learning analysis.

scmTE infers gene regulatory networks from scRNA-seq data by reducing false predictions, identifying known and novel regulatory interactions in biological processes.

This study uses deep learning to control gene expression dynamics in E. coli cells, enabling real-time, cell-specific regulation of gene expression patterns.

The paper introduces a divide-and-conquer method to solve the Chemical Master Equation in high-dimensional gene expression systems, demonstrating its effectiveness in analyzing yeast transcription at single-cell resolution.

WEBNG simplifies rare event sampling in rule-based models by integrating BioNetGen and WESTPA, enabling efficient simulation with default parameters.

This study introduces a genome-wide classification of regulatory modes in bacteria by analyzing transcription-replication interactions, revealing new insights into gene expression dynamics and regulatory context.

RNetDys identifies disease-related impaired regulatory interactions due to SNPs by analyzing multi-OMICS data and cell-type specific GRNs.

The paper analyzes how gene expression can identify and assess extracellular stimuli encoded in transcription factor nuclear concentration amplitude, duration, or frequency, finding frequency modulation is most selective for stimulus identification.

This study shows how cell density affects signal transduction in synthetic gene circuits, enabling spatial and temporal control of multicellular patterning.

The paper analyzes how protein expression-growth coupling amplifies stochasticity in gene expression and quantifies the resulting noise and skewness in protein concentration.

BoostDiff identifies differential gene regulatory networks between disease and healthy states, outperforming existing methods on simulated and real transcriptomics data.

The paper investigates how allelic transcription factor binding influences transcriptional kinetics in human cell lines using scRNA-seq, finding strong correlations between eQTL effect sizes and burst frequency.

This paper introduces PMF-GRN, a variational inference method for single-cell GRN inference, showing improved accuracy and uncertainty estimates compared to existing methods.

SCING improves GRN inference accuracy and interpretability using gradient boosting and mutual information on scRNAseq, snRNAseq, and spatial transcriptomics data.

Ingres fits probabilistic Boolean networks to single-cell RNA-seq data, capturing cell phenotype differences using continuous protein activity measures.

The paper introduces SARGENT, a method to measure gene expression noise and mean expression across the genome, finding correlations between noise and histone modifications, and linking extrinsic noise to cell sub-states.

The paper shows E. coli cells use the cAMP-CRP system to continuously adjust catabolic enzyme expression in response to metabolic fluctuations, revealing noise propagation modes and their regulation.

The paper quantifies the mean expression levels in linear CRNs under a random environment, showing how heterogeneity affects the stationary mean.

This paper discusses mathematical models for gene regulatory systems, highlighting the advantages of stochastic models over deterministic ones for accurate prediction.

This paper develops a framework using split inteins to design and test biomolecular controllers that achieve robust perfect adaptation in mammalian cells.

The paper uses optogenetic control of RelA to show its nuclear translocation alone can activate downstream genes, revealing dynamic gene responses and the need for complex promoter models.

This study introduces a Bayesian Markov Random Field model to infer gene network edges, considering both existence and strength, and demonstrates its performance on glioblastoma data.