Gene Regulatory Network Analysis

This paper identifies bifunctional transcriptional effector domains that generate gene expression pulses by switching from activation to repression based on DNA occupancy, and develops a kinetic model to program cell states.

The paper shows that drug response predictors trained on perturbation response model-generated profiles outperform those trained on baseline or pre-treatment profiles.

The paper shows that combining three indirect repression mechanisms maintains ultrasensitivity and noise robustness in transcriptional switches under realistic biological conditions.

Airqtl accelerates single-cell eQTL mapping and enables cell state-specific causal gene regulatory network inference using efficient linear mixed models.

This study introduces a generative model to infer epigenetic landscapes from data, demonstrating its application in developmental biology examples.

This paper benchmarks causal structure learning algorithms for gene regulatory network inference using synthetic and CRISPR perturbation data, finding reliable performance on synthetic data but limitations on real data due to noise and scalability.

This paper uses reinforcement learning to control a bi-stable genetic system, showing improved accuracy and efficiency compared to existing methods.

This paper introduces and analyzes an antithetic dual-rein integral feedback controller for stochastic chemical reaction networks, showing it can control first moments and maintain tunable variance bounds.

This paper introduces a protocol for characterising genetic circuits using feedback control to predict their performance when combined with other components.

The paper designs and engineers minimal protein-based molecular inverters and switches that respond to ligands, demonstrating tunable and modular control of post-translational signaling.

BioLogical is an R package for analyzing biosystem logical dynamics, capable of multi-valued logical analysis, order parameter calculation, and system simulation.

The paper introduces INF, an algorithm that integrates protein and gene level information to analyze regulatory circuits and their role in cellular responses and identity.

This paper analyzes how ribosome competition affects protein neural networks in bacterial systems, showing it alters decision boundaries and output magnitudes.

The paper identifies universal scaling patterns in bacterial cells' stochastic responses to environmental changes across different temperatures.

LimbNET provides a collaborative platform for simulating and visualizing gene network patterns in limb development using spatiotemporal data.

This paper establishes design principles for implementing neuromorphic computing in living cells using genetic circuits, demonstrating how four chemical reaction networks can function as perceptrons and be layered to create decision boundaries for cell classification.

This paper introduces a computational framework to reconstruct developmental landscapes from single-cell data, revealing morphogen-dependent cell fate transitions in neural progenitors.

The paper shows that ligand discrimination can occur at the population level through response heterogeneity in dimeric and heterodimeric receptor systems.

The paper introduces a model linking chromatin and enhancer states to transcriptional bursting dynamics, using the Monod-Wyman-Changeux framework to predict bursting behavior in C. elegans.

The paper demonstrates that increasing activator expression can reduce target gene expression by displacing repressor from decoy sites, explaining paradoxical gene regulation through competition for genomic binding sites.

GRNITE improves gene regulatory network inference by combining text embeddings and graph neural networks, enhancing AUROC and recall across methods.

The paper models how protein sequestration affects gene expression noise, showing that sequestration cooperativity and kinetics alter the distribution of free protein.

The paper models NF-κB epigenetic variability in breast cancer, showing it drives subtype transitions and heterogeneity through attractor basins.

The paper identifies trans-acting mutations in yeast that modulate gene expression noise in both intrinsic and extrinsic ways, revealing non-nuclear factors influencing nuclear gene expression.

AlterNet infers transcript-level gene regulatory networks by considering alternative splicing, revealing novel regulatory interactions not detectable at the gene-level in heart tissue data.

This study identifies five classes of endocrine circuits in humans, revealing shared structural and regulatory principles across 43 hormone systems.

The paper introduces a multilevel Boolean model to capture hybrid epithelial-mesenchymal phenotypes, revealing more stable and biologically relevant hybrid states than traditional binary models.

CASCADE is a framework for causal regulatory inference and rational transcriptomic reprogramming design, enabling causal discovery and intervention design for targeted cells.

The paper identifies a dynamic gene regulatory network in Duchenne muscular dystrophy that controls cell fate decisions and shows how disease mutations disrupt this network.

The paper develops a noise decomposition framework to distinguish intrinsic gene-gene coupling from extrinsic noise in stochastic transcription using single-cell data from Drosophila embryos.