Gene Regulatory Network Analysis

This study examines plasmid crosstalk in cell-free biosensors and shows how it can be leveraged to improve biosensor performance.

The paper extends a biochemical modeling framework to large-scale genetic and metabolic networks using toric geometry and algebraic varieties.

This paper develops a computational framework to simulate hepatic lineage induction through gene regulatory and functional dynamics, showing that direct programming achieves stable hepatic states faster than reprogramming.

The paper improves gene regulatory network inference by integrating cell cycle positions into oscillatory network analysis, showing better performance for early progenitor cells.

The paper introduces tools for parameter estimation of biochemical networks using stability constraints, validated on MTOR network data from breast cancer cells.

This paper identifies a promising therapeutic target pair for TNBC using a hybrid approach combining SDP, Boolean simulation, and druggability analysis, achieving higher druggability and lower complexity than prior methods.

This paper models and minimizes dCas9 competition in CRISPRi-based genetic circuits to improve their performance and dynamic range.

The paper quantifies age-related decline in transcriptional information flow using information theory and single-cell RNA-seq data, finding reduced mutual information between transcription factors and target genes.

This paper evaluates 20 Boolean modelling tools for signal transduction and gene regulatory networks using FAIR4RS and usability criteria, finding significant variation in functionality and simulation capabilities.

The paper identifies context-specific failures of the Cre recombinase in plant gene circuits and develops improved recombinase systems for stable memory functions.

This paper develops orthogonal inducible transcriptional control systems in yeast to study dosage-dependent gene-gene interactions, confirming known interactions and revealing new phenotypes.

This study shows single macrophages can accurately detect and respond to short-lived immune signals through receptor cooperativity and pathway variability.

The paper identifies two optimization approaches for designing near-homeostasis systems and introduces inverse homeostasis plots to uncover novel mechanisms.

This paper presents an inversion method to recover microscopic rate constants from Michaelis-Menten parameters in enzyme kinetics, with applications to biochemical pathway modeling.

The paper identifies division asymmetry as the main driver of cell size variability in budding yeast, showing it outweighs G1/S size control effects.

The paper introduces a method to identify indicator molecules in biochemical reaction networks that can distinguish between multiple steady states without kinetic parameters.

The paper introduces TFA (Transcription Factor Activity) to infer TF-TF interactions with high fidelity, outperforming expression-based methods and revealing isoform-level heterogeneity in regulatory circuits.

The paper analyzes monotone Boolean network models of gene regulatory networks to identify common steady states and multistability patterns, showing epithelial and mesenchymal states are most common in the EMT network.

The paper introduces ctsGRN, a framework for inferring cell type-specific gene regulatory networks in Arabidopsis roots using multiple computational methods, and identifies key transcription factors and regulatory modules.

This paper develops a hybrid neural-symbolic framework to uncover the mathematical principles linking cellular energetics to genetic regulation using simulated time-series data.

The paper develops a mathematical framework showing intercellular signaling stabilizes cell fate and tissue patterning by suppressing spontaneous switching and inducing coordinated behaviors.

The paper shows that molecular signaling networks can resist temperature and ionic strength changes through topological compensation using compensatory reactions.

The paper shows that multi-team competition in gene regulatory networks leads to less stable outcomes compared to binary competition, explaining why binary decisions are common in biology.

The paper analyzes gene regulatory networks across 9,000 tumors to identify recurrent PD-1 pathway dysregulation linked to patient outcomes.

This paper engineers synthetic epigenetic circuits that induce stable gene expression changes and drive cell state transitions through transient cell-cell interactions.

This paper introduces RiCE, a physics-based algorithm for inferring gene networks from time series data, showing improved performance across multiple datasets and applications.

The paper identifies a noise-driven differentiation switch mechanism that controls cell type diversity in neural progenitor cells through combinatorial signaling inputs.

This paper introduces in-CAHOOTTS, a neural ODE framework for inferring gene regulatory networks from dynamic single-cell data, showing it can predict gene expression and identify regulators in yeast responses to rapamycin and cell cycle progression.

This study uses a mathematical framework to analyze how stochasticity and nonlinearity influence gene regulatory network motifs, showing how fluctuations affect cell fate decisions.

This paper introduces a method using Chatterjee's rank correlation to construct gene regulatory networks from scRNA-seq data, outperforming existing methods in capturing complex dependencies.