Gene Regulatory Network Analysis

scLEMBAS models signaling pathway activity at single-cell resolution using a context-aware neural network to predict perturbation responses and capture cell-type-specific interactions.

This paper introduces flux-species sampling (FSS) to explore biocircuit behavior by directly sampling steady-state species and fluxes, improving the identification of regulatory modes and behavior-realizing regimes.

The paper explains how weak-affinity ligands can drive broader signaling activity than strong-affinity ligands by showing that receptor architecture decouples signal strength and range through kinetic proofreading.

BaiZe predicts and interprets cellular responses to perturbations using a multi-view framework, showing effectiveness across diverse contexts and improved predictions with ATAC-seq data.

This paper introduces REGA, a framework that uses reference regulatory elements to infer gene regulatory networks from expression data, showing improved performance across multiple benchmarks and applications.

The paper investigates how bacteria control flagellar number through assembly-coupled feedback, showing robustness arises from a balance between intrinsic fluctuations and regulator variability.

This paper engineers a multi-step differentiation program in E. coli to generate self-organized spatial patterns through symmetry breaking, paracrine, and autocrine signaling.

This paper introduces Giraffe, a matrix factorization method for gene regulatory network inference that estimates signed regulatory effects, showing improved accuracy in distinguishing activating and inhibitory interactions across multiple biological contexts.

This study identifies network structures that support multi-fate cellular decisions by analyzing gene regulatory networks using monotone Boolean models.

The paper shows that sampling biases in negative regulatory interactions inflate performance in gene regulatory network inference and proposes a degree-aware sampling approach to address this issue.

The paper develops a theoretical framework showing gene regulatory networks (GRNs) enable pattern scaling during tissue growth without relying on morphogen scaling.

This paper develops a control framework for chemical reaction networks to robustly track multiplicative combinations of species concentrations and validates it with simulations.

This study shows that negative autoregulation in gene circuits enables stronger environmental switching by favoring alleles that create strong molecular switches in E. coli.

This paper introduces WENDy, a method for parameter estimation in systems biology models using weak-form regression, demonstrating its application on real experimental data for glycolytic oscillators and cell-state transitions.

The paper models the Mecom-Cdk6 regulatory network to show how IGF signaling and self-activation shape stem cell fate transitions through multistable landscapes.

PARROT detects change-points in dynamic gene regulatory networks using Stochastic Block Models, identifying module reassignments and connection changes in human and mouse data.

This paper benchmarks predictive modeling and control techniques with optogenetics to steer nucleocytoplasmic transport in mammalian cells, showing robust performance against cell-to-cell variation.

ORIGAMI reconstructs cis-regulatory networks using multi-omics data and deep learning, revealing synergistic transcription control and disease-associated rewiring.

The paper designs a synthetic bacterial system that generates self-organized traveling waves through integrated feedback and diffusible signals, showing how circuit architecture and diffusion influence pattern dynamics.

This paper introduces a method to expand gene regulatory networks by incorporating prior knowledge, outperforming conventional methods in distinguishing true interactions from indirect associations.

The paper introduces FICUS, a framework that integrates omics data with logic models to identify shared signaling mechanisms across patient subgroups.

The paper models how cell division dynamics influence heterogeneous contact-mediated signaling outputs in growing cell collectives.

This paper introduces an ultrasound-responsive gene regulation system in bacteria that uses an AND-gate mechanism combining ultrasound and rapamycin to control gene expression in a microbial consortium.

This paper introduces a thermodynamic framework for reducing enzyme cycle models while preserving physical constraints, applicable to both hypothetical systems and real data like Na+/K+ ATPase.

This paper develops a Dynamic Bayesian Network model to analyze multi-fault behavior in signaling pathways and prioritize drug interventions based on efficiency and burden scores.

RICE improves causal gene network estimation in single-cell CRISPR screens by addressing confounding, count data, and high-MOI designs, outperforming existing methods in synthetic and real data.

This paper introduces CyTRACE, a genetically encoded framework for recording cytosolic activity and archiving molecules over time in mammalian cells.

This study compares transcription bursting patterns between scaffold and effector proteins in multi-protein complexes, finding that scaffold proteins use more frequent, smaller bursts to maintain homeostatic expression.

The paper shows that bacterial colonies generate traveling waves of gene expression driven by self-generated environmental feedback during growth and development.

The paper challenges the idea that dose-response alignment enhances information transmission in signaling pathways, showing it is dissociable from linearity and information transmission.