Gene Regulatory Network Analysis

This paper introduces a spline-based method for parameter estimation in ODE models using semi-quantitative data, improving estimation efficiency and enabling discovery of unknown measurement transformations.

MORE constructs phenotype-specific multi-omic regulatory networks using regression-based modeling and variable selection, showing improved accuracy and efficiency over existing tools.

This study develops a computational framework to model M1 and M2 macrophages in RA, identifying potential therapeutic targets through Boolean modeling and gene expression validation.

This paper introduces a computational framework to decompose biochemical reaction networks into timescales, validating it on kinetic models and glycolytic pathways to reveal biologically interpretable dynamics.

This paper introduces a deep learning framework for gene regulatory network inference and latent transcription factor activity estimation, showing improved performance over existing methods.

MCLand is a Python program that visualizes Waddington's epigenetic landscape using Monte Carlo simulations to model gene regulatory networks and estimate cell state probabilities.

This study uses targeted Perturb-seq to identify EGR1 and FOS as key regulators of the transcriptional RAF-MAPK response by constructing a directional interaction network.

This paper develops a rule-based cell cycle model for RPE-1 cells that explains 16 observables and improves model reusability through PEtab specification.

The paper develops a stochastic Boolean model of the budding yeast cell cycle that accurately replicates wild-type and mutant phenotypes, providing a validated framework for studying cell cycle regulation.

The paper compares gene regulatory networks across seed plants and an alga, finding conserved regulators of photosynthesis and differences in regulatory mechanisms between species.

SPREd is a simulation-supervised neural network for gene regulatory network reconstruction that outperforms existing methods on synthetic and real yeast datasets.

The paper introduces a framework for designing cell-type-specific promoters using model-based optimization, demonstrating improved specificity in blood cancer cell lines.

This paper develops a theoretical framework to model enhancer-promoter communication and its impact on transcriptional bursting, revealing power-law relationships and novel distribution patterns.

This paper introduces cascaded antithetic integral feedback to improve dynamic performance and robust stability in synthetic biology systems.

The paper uses sensitivity analysis and support vector ranking to prioritize higher-order interactions in time series Wnt pathway data, revealing how these interactions change over time.

The paper quantifies and models allelic transcription dynamics of GREB1 in response to estradiol using smFISH and statistical methods.

This paper introduces a programmable reaction-diffusion system using MinD and MinE proteins to engineer spatiotemporal signaling in mammalian cells, enabling frequency-barcoded fluorescence signals for analyzing cellular activities.

The paper combines mathematical modeling and experimental analysis to show that bacterial cell division is based on surface area, not size, leading to stable colony growth.

The paper develops Density-PINN to infer transduction-time distributions from single-cell gene expression data and finds that heterogeneity in response intensity is reduced when multiple signaling pathways regulate the response.

This paper develops a method to identify changes in buffering structures of chemical reaction networks caused by single degradation reactions, enabling prediction of regulatory changes without kinetic assumptions.

The paper introduces a cybernetic-inspired modeling framework to analyze the transcriptional regulation of the mammalian cell cycle, identifying functional network modules and predicting future cell cycles.

The paper analyzes experimentally-supported biomolecular networks to show they are ordered and far from the edge of chaos, with new measures and simulations revealing greater rigidity than previously thought.

The paper introduces software that optimizes transcription factor networks to buffer stochasticity and entrain to erratic circadian signals, finding multiple solutions that highlight the flexibility of overparameterized systems.

The paper investigates how energy metabolism affects the regulatory impact of activators on gene expression using a mathematical model and experimental validation in Drosophila.

This paper derives an exact solution for a three-stage stochastic gene expression model that includes cell-cycle dynamics, showing discrepancies with the classical model that ignores cell-cycle effects.

This paper provides a theoretical synthesis of biphasic responses in cell signalling systems, revealing design principles and structural factors that enable or preclude such responses.

The paper studies transcription attenuation in synthetic tandem promoters in E. coli, finding that promoter positioning and factors like rifampicin influence attenuation through RNAP collisions.

This study applies reinforcement learning to optimize synthetic biological networks by controlling Hopf bifurcations in ODE-based systems, showing its ability to identify bifurcation onset while facing challenges in persistent oscillations and computational speed.

This paper introduces a novel moment closure scheme using Model Predictive Control to accurately predict the time evolution of moments in the Chemical Master Equation, outperforming existing methods.

The paper introduces FGES-Merge, a method for learning large gene regulatory networks from brain data, achieving high accuracy and speed while providing interpretable results.