Gene Regulatory Network Analysis

The paper analyzes single-cell dynamics during state transitions, finding that cell states are sensitive to few 'stiff' parameters and robust to many 'sloppy' parameters, suggesting a principle of least action.

This study evaluates foundation models for predicting cellular responses to perturbations, finding that some pretrained embeddings improve predictions while others do not.

ADAPRE infers causal gene regulatory networks from Perturb-seq data by using adaptive instrumental variable modeling, improving accuracy over existing methods.

The paper develops a new chemical dynamics model to quantify adaptive gene expression in E. coli, revealing a quadratic relationship between mean and variance of activation times.

The paper investigates how cell-state differences affect drug responses and signaling network interactions in human epidermal stem cells.

The paper investigates how host-circuit coupling and resource competition drive spatiotemporal plasticity and multistability in genetic circuits, with implications for cancer progression.

This paper investigates how protease competition leads to bistability and spatial pattern formation in gene regulatory systems through spatio-temporal analysis.

This paper introduces a computational framework for designing mammalian genetic programs using genetic algorithms and dynamical systems models, demonstrating its ability to find optimal circuit designs and validate them experimentally.

This paper designs synthetic genetic circuits to implement non-associative learning behaviors in unicellular and multicellular systems.

The paper shows that causal relationships can exist in transcriptional networks without correlation, using nonlinear causality tests to detect uncorrelated causal links in yeast and mouse.

This paper introduces a computational formalism that enables emergent threshold dynamics through bidirectional coupling of metabolic state and regulatory control, demonstrating spontaneous homeostasis and adaptive reorganization in drug transport.

This paper introduces a method to predict reversible and irreversible cell state transitions using gene regulatory networks and gene expression data.

This paper introduces a genetic feedback control strategy for microorganisms to self-regulate population density using quorum sensing and an antithetic controller.

The paper models the eukaryotic cell cycle as a tetra-stable excitable system using ODEs, phase plane analysis, and bifurcation theory, showing that two existing frameworks represent the same underlying architecture.

This paper develops a multiscale model, GEAGS, to predict transient oscillations in synthetic gene circuits by coupling gene expression to growth phases, revealing amino acid recycling and growth transitions as key factors.

This paper develops a data-driven computational model of the human cell cycle that predicts inhibitor effects and combination responses in silico.

scMagnify decomposes multi-scale gene regulatory networks from single-cell multiomics data, revealing regulatory logic in development and disease.

The paper introduces a coherence matrix to analyze directional regulatory coordination in gene regulatory networks, showing how hierarchical structures mitigate scaling penalties in sparse networks.

The paper introduces a topological framework for analyzing genetic and metabolic networks, independent of kinetic parameters, and applies it to circadian rhythm gene circuits.

This study identifies sudden transitions in Notch signaling using early warning signals and analyzes how system parameters influence these transitions in one- and two-cell models.

This paper introduces SCCVAE, a hybrid model combining mechanistic causal models with deep learning to predict and simulate genetic perturbation responses in single-cell data.

The paper develops a graph neural network to predict transcription factors for direct reprogramming by integrating gene regulatory networks with microRNA-mediated regulation.

The paper develops a workflow to identify and minimize biological variability in transcriptional activation assays using mathematical modeling and sensitivity analysis.

The paper investigates the relationship between gene expression plasticity and regulatory network topology in Escherichia coli, finding that plastic genes are regulated by more genes but not by specific network motifs.

This paper introduces CytoTape, a tool for continuous, multiplexed recording of gene regulation dynamics over weeks at single-cell resolution, and demonstrates its use in measuring transcription factor activities.

The paper introduces the Coli Toolkit (CTK), a modular cloning system for E. coli, expanding on the Yeast Toolkit to enable efficient genetic circuit assembly.

The paper introduces a host-aware framework to identify transferable gene expression biopart parameters from combinatorial libraries, revealing intrinsic properties of transcriptional units.

ReCoN models multicellular coordination by integrating cell-cell communication and intracellular regulation to predict cellular responses to perturbations.

This paper introduces a moments-based framework to infer transcriptional kinetics from nascent RNA data, showing improved accuracy in model selection and distinguishing promoter activation mechanisms.

This study identifies central transcription factors in lung adenocarcinoma progression by analyzing gene regulatory network transitions and reveals their role in intratumor heterogeneity.