Gene Regulatory Network Analysis

This paper develops a spectral theory and computational methods to analyze frequency spectra of noisy cellular networks, enabling topology discrimination and synthetic oscillator optimization.

The paper uses single-cell multi-omics data to predict gene regulatory networks in Alzheimer's disease and control, identifying cell-type-specific dysregulation and potential drug targets.

CELLoGeNe maps Boolean gene regulatory networks into energy landscapes to analyze cell fate decisions, identifying attractors in pluripotency maintenance and reprogramming.

The paper introduces fides, a Python framework for ODE model calibration using trust-region optimization, showing improved reliability and efficiency over existing methods.

This paper introduces an extrinsic chemical Langevin equation to model biochemical systems with both intrinsic and extrinsic noise, showing improved computational efficiency while maintaining accuracy.

DelaySSAToolkit.jl provides a Julia package for simulating reaction systems with time delays using exact formulations of the delay stochastic simulation algorithm.

The paper introduces a synthetic model-based approach for uncertainty quantification in stochastic gene expression models, showing improved performance over existing methods.

The paper develops and compares four dynamical models of a pathogen-infected plant gene regulatory network to determine the best model for capturing its dynamics.

The paper develops a control strategy to balance cell phenotypes in microbial consortia using reversible memory mechanisms and validates it through simulations.

This paper classifies resource competition structures in synthetic gene circuits and shows how their response to competitors and interventions can distinguish between different competition mechanisms.

This paper introduces Control-Based Continuation (CBC) for analyzing synthetic gene networks, showing it can track bifurcation curves without relying on mathematical models and can identify bistability.

The paper develops an open-source, scalable mechanistic model for single-cell proliferation and death signaling, demonstrating its application to understand IFNγ's anti-proliferative effects.

NETISCE identifies cell fate reprogramming targets in static networks using Signal Flow Analysis and Feedback Vertex Set Control, validated across developmental, stem cell, and cancer biology studies.

The paper benchmarks imputation methods for gene regulatory network inference using synthetic scRNA-seq data generated by a novel tool.

The paper introduces ReacSight, a system for enhancing bioreactor arrays with automated measurements and reactive control, demonstrating its use in yeast cultures for gene expression control and competition assays.

The paper analyzes how gene regulatory network topology influences noise propagation, identifying conditions for noise reduction in ring networks and proposing a framework using graph theory and stochastic processes.

The paper shows that optimizing receptor localization in cells enhances information acquisition in spatially structured environments, improving navigation efficiency by 30-fold through dynamic redistribution.

The paper introduces algorithms to identify extreme signaling failures in molecular networks and finds that specific molecules, not random ones, cause the most detrimental network dysfunction.

The paper develops a recurrent neural network to simulate intracellular signaling by predicting transcription factor activity from ligand concentrations, validated with synthetic and experimental data.

PEPSDI infers model parameters from time-lapse data to identify extrinsic noise sources in yeast nutrient sensing pathways.

This paper develops a Bayesian framework for learning dynamic Bayesian networks from time series gene expression data to discover gene regulatory networks in multiple phenotypic groups, showing improved classification accuracy in cancer and therapy response datasets.

LOICA is a Python package for modeling genetic circuits using object-oriented design, linking high-level designs to experimental data via SynBioHub and Flapjack.

The paper develops a modular toolset to improve the performance and standardization of electrogenetic systems for spatiotemporal gene expression control.

The paper shows that mitotic kinase oscillations control cell cycle latches, and disrupting these leads to endocycles in yeast.

This study uses machine learning to uncover the genome-scale transcriptional regulatory network in Pseudomonas putida using gene expression data.

The paper models the C. elegans germline stem cell genetic network using formal reasoning to predict regulatory interactions and cellular decision-making.

The paper develops a stochastic model to analyze how time-dependent stimuli affect mRNA fluctuations in different sub-cellular locations and derives analytical distributions for mRNA numbers at various stages.

BioCRNpyler compiles chemical reaction networks from modular biochemical parts, enabling rapid model development and exploration across diverse contexts.

This paper applies and improves modular response analysis to infer gene regulatory networks in large biological systems with over 1,000 components.

This paper designs biomolecular PID controllers to achieve robust perfect adaptation and improved dynamic performance in synthetic biology systems.