Gene Regulatory Network Analysis

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.

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

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

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

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.

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.

Talk2Biomodels provides an AI platform for interacting with biological models using natural language, enhancing accessibility and FAIR principles.

The paper quantifies how cancer and drug treatments affect the information capacity of RTK signaling pathways using optogenetic stimulation and information theory.

The paper develops a mechanistic framework showing how cell cycle criticality enables robust population control by balancing self-renewal and differentiation through sensitivity to mitogenic signals.

The paper introduces a non-equilibrium model explaining how signaling receptors achieve ligand specificity through multi-site phosphorylation and degradation, showing that intermediate-affinity ligands maximize network activity.

The paper quantifies information transduced through the ERK pathway in single cells and populations, showing single cells can resolve graded stimuli with over 2 bits of information.

This paper introduces Dropout Augmentation and DAZZLE, a GRN inference method that improves performance by addressing dropout in single-cell data through model regularization.

QWENDY infers gene regulatory networks from single-cell gene expression data across four time points, outperforming existing methods on experimental data but not synthetic data.

The paper examines how JNK activation dynamics influence gene expression patterns through mRNA stability and pathway-specific regulation.

This paper develops a computational pipeline to infer kinetic parameters from single-cell RNA sequencing data and assesses parameter identifiability using the stochastic telegraph model.

scTFBridge infers gene regulatory networks from single-cell multi-omics data by integrating TF-motif binding knowledge, outperforming baselines in cis- and trans-regulation tasks.

The paper develops a data-informed model of AMPK signaling using uncertainty quantification and biosensors, showing AMPK integrates inputs over time during exercise.

RepliFlow infers cell cycle dynamics from flow cytometry data, capturing changes in DNA replication program alterations.

The paper develops a framework to integrate multiplexed immunofluorescence data with mathematical models to study cell cycle dynamics and arrest, validated on MCF-10A cells exposed to arresting agents.

The paper introduces a framework to analyze gene regulatory networks by identifying mutually inhibiting teams of nodes and shows how network topology predicts dynamic behavior.

The paper introduces locaTE, a method to infer cell-specific gene regulatory networks using single-cell dynamics and geometry, showing improved performance over existing methods.

This paper designs and evaluates minimal genetic circuits that enable cells to anticipate environmental trends through deterministic and stochastic analyses.

The paper introduces GRN_modeler, a user-friendly tool for constructing and evaluating gene regulatory networks, demonstrating its application in designing oscillators and a light biosensor in E. coli.

The paper introduces BoNesis, a tool for inferring Boolean networks from transcriptome data to model cellular differentiation and reprogramming, demonstrating its application in hematopoiesis and adipocyte/osteoblast differentiation.

PyRID is a Python-based reaction-diffusion simulator for molecular biological systems, capable of simulating reactions and interactions between proteins and substrates.