Gene Regulatory Network Analysis

This paper presents a distributed algorithm for microbial whole-cell sensors inspired by the Allee effect, showing it works correctly in noisy environments through mathematical analysis and simulations.

ACCORDION selects relevant knowledge and recommends models by integrating literature retrieval, graph algorithms, and simulation, improving accuracy and reducing errors in biological systems.

The paper establishes an upper bound on the ultrasensitivity of biochemical reaction cascades using Hill coefficients and tests it across different sigmoidal functions.

The paper benchmarks algorithms for inferring gene regulatory networks from single-cell RNA-seq data by evaluating their ability to capture topological properties and identify hubs.

The paper analyzes experimental data on fission yeast cell cycle transitions and proposes a stochastic model to assess bistability of the G2/M checkpoint.

The paper compares stochastic growth and division dynamics of bacterial cells in two microfluidic platforms, finding that mechanical confinement increases interdivision times but is compensated by division ratio adjustments.

The paper develops a computational approach to model core gene regulatory networks in IDH mutant AML, identifying key regulators like DNMT1 and E2F1 supported by patient survival data.

This paper presents an automated method to design gene regulatory mechanisms that produce spatial patterns using two morphogen gradients and evolutionary computation.

The paper uses a stochastic model to fit single-cell transcriptomic data and shows it can recapitulate mRNA distributions during B cell differentiation in germinal centers.

The paper evaluates how different regulatory logic rules affect the structural features of Boolean network state transition graphs, finding that biologically meaningful rules improve bushiness and convergence.

Cell4D is a spatial stochastic simulator for cellular pathways that enables hypothesis generation and testing through interactive visualization and high-performance computing.

The paper analyzes Boolean functions in biological networks and finds generalized chain functions are significantly enriched in NCFs across multiple datasets.

This paper introduces a noise-resilient ultrasensitive transcriptional switch using indirect repression mechanisms and shows its effectiveness in noisy cellular environments.

This study investigates transcriptional logic out of equilibrium and finds that non-equilibrium binding of transcription factors enables new logic operations not accessible in equilibrium.

This paper introduces SCORPION, a tool for reconstructing gene regulatory networks from single-cell RNA-seq data, showing it outperforms existing methods and detects consistent regulatory differences in colorectal cancer.

The paper presents a coarse-grained E. coli cell model to analyze and design synthetic gene circuits by capturing resource couplings and growth regulation mechanisms.

The paper introduces a genetically stable kill-switch using a 'demon and angel' construct of an essential gene in E. coli, which conditionally kills cells through toxic overexpression while maintaining viability in the OFF state.

This study applies SINDy to biological oscillatory data, identifies limitations in resolution, noise, dimensionality, and prior knowledge, and proposes a guide for model inference using yeast glycolytic oscillation data.

This study provides an exact time-dependent solution for the CME of a gene circuit with autoregulation and bursty expression using spectral decomposition.

The paper shows that noisy delay in genetic oscillators can improve temporal peak reliability and sharpen oscillation peaks without affecting the period.

The paper quantifies landscape-flux using single-cell transcriptomics to uncover cell cycle mechanisms and identify key genes involved in maintaining dynamics.

This study uses Boolean network models to identify therapeutic targets in breast cancer by simulating transitions between cancer and apoptosis attractors using RNA-seq data.

This study shows that protein exchange between compartments affects the steady states and stochastic switching in a two-site phosphorylation network, with switching rates peaking at intermediate exchange rates.

The paper develops a continuous epistasis model to predict E. coli growth rate based on gene expression and environmental variations using experimental data.

The paper uses single-cell experiments and stochastic modeling to uncover mechanisms of GR dynamics and DUSP1 transcription regulation in response to glucocorticoids.

The paper quantifies the distribution of sensing abilities in cell populations using an information theoretic framework and shows most cells achieve better input resolution than the average.

The paper demonstrates a single DNA molecule-based genetic circuit that functions as a dissipative nanodevice by enabling coexpressional localization of proteins.

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.