Gene Regulatory Network Analysis

This paper uses Bayesian multimodel inference to improve predictive certainty in systems biology models of intracellular signaling pathways.

ChEA-KG constructs a human transcription factor regulatory network using gene set enrichment analysis and provides an interactive web interface for network exploration and analysis.

This paper introduces a Python framework using ODEs and Hill functions to simulate bistability in gene regulatory networks, showing how parameters affect system dynamics and attractor behavior.

The paper identifies small molecules that enhance gene expression in synthetic circuits by analyzing RNA-seq data and drug-induced profiles.

The paper designs and evaluates recombinase-based genetic programs in human cells that simulate a for-loop counting to three, demonstrating heritable memory and sequential gene expression.

The paper introduces MIDOE, a method to design experiments that optimally constrain model predictions of Smad2-Smad4 dynamics, improving predictive power and reducing experimental costs.

Epiregulon infers single-cell TF activity using ATAC-seq and RNA-seq to predict drug response and identify drivers of cell state.

PGBTR is a computational framework that uses CNN to predict bacterial transcriptional regulatory networks, outperforming existing methods on Escherichia coli and Bacillus subtilis datasets.

This paper introduces Structural Bifurcation Analysis (SBA) to identify bifurcation behaviors in biochemical networks from topology, applied to macrophage polarization revealing structural constraints and gene-dependent patterns.

D-SPIN constructs regulatory network models from single-cell perturbation data by integrating multiple conditions and inferring gene or program-level interactions.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

The paper designs a two-input sensor that combines chemical and temperature inputs to regulate gene expression, functioning as an AND Gate with a measured asymmetry.

The paper models a multicellular antithetic integral controller to assess metabolic burden effects and compares it to a single-cell implementation using numerical analysis and simulations.

This study evaluates two threshold formalisms for Boolean gene regulatory networks, finding that while Ising rules match fewer functions exactly, they provide better average agreement with biological dynamics.

This paper introduces a synthetic biomolecular controller to guide cell fate decisions by mimicking in vivo regulatory dynamics, achieving tunable bias toward desired cell types with minimal network alteration.

This study uses single-cell RNA sequencing and the SCENIC pipeline to analyze gene regulatory networks in P4HA1-knockout mice, identifying unique regulatory patterns in control mice linked to stem cell and inflammatory pathways.

This paper introduces a co-evolutionary framework for jointly inferring gene regulatory network topology and parameters, showing improved performance on synthetic benchmarks.

The paper argues that subpopulations in single-cell data may not reflect distinct behavioral modes and shows that noise can create false subpopulations from a single mode.

The paper develops a multiscale computational model integrating metabolism, growth, and cell cycle in yeast to predict that fermentation modulates cell size under different growth conditions.

The paper infers high-resolution developmental time from fixed Drosophila embryos using deep learning to study gene regulation dynamics without genetic modification.

This paper introduces GRIT, a method for inferring gene regulatory networks from single-cell data using optimal transport to model the evolution of cell distributions over time.

The paper identifies a bistable circuit regulating miRNA-155 levels in human macrophages during inflammatory transitions using multiomic and systems biology approaches.

This study measures noise and plasticity in protein expression of essential and nonessential genes in E. coli, finding essential genes have lower noise and plasticity, and nonessential genes show a positive correlation between them.

The paper derives an exact steady-state solution for the joint distribution of nuclear and cytoplasmic mRNA levels and uses it to quantify bursty gene expression in human cells.

The paper identifies a Fitness-Entropy Compensation mechanism that stabilizes cell populations by balancing growth and gene expression through increased expression diversity.

The paper infers ecological networks and dynamics using Boolean models from observations and prior knowledge, demonstrating its ability to recover essential interactions in a protist system.

This paper develops thermodynamically-consistent, reduced models of gene regulatory networks for scalable agent-based simulations of cell populations.

ScReNI integrates scRNA-seq and scATAC-seq data to infer cell-specific regulatory networks and identify cell-enriched regulators.

SPONGE generates prior gene regulatory and protein-protein interaction networks using biological databases to enhance network inference tools like PANDA.

This study evaluates threshold majority rules in gene regulatory network modeling, finding they lack biological realism and fail to capture key dynamics observed in empirical data.