Gene Regulatory Network Analysis

This paper introduces a memory quantifier to assess molecular memory in gene regulatory networks and validates it using mouse embryonic stem cells.

This study evaluates evolutionary algorithms for estimating reaction kinetic parameters under different noise conditions and identifies algorithm-specific effectiveness.

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.

This study models CD8 T cell immune response using a multiscale agent-based approach to link genetic regulation to population dynamics.

This paper introduces a method to design high-performance mammalian whole-cell biosensors by integrating synthetic biology and control engineering, resulting in a versatile biosensor named CASense.

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.

The paper uses stochastic models to show how the location of negative feedback loops in immune signaling affects gene expression noise, finding that downstream loops reduce noise while upstream loops amplify it, which can be mitigated by strong positive feedback.

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.

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 studies a four-node mutually repressive network, showing it allows for six stable double-positive states and applies this to explain Th1/Th2/Th17/Treg differentiation.

The paper introduces AC-BioSD, a biomolecular signal differentiator with improved performance in signal differentiation and noise attenuation.

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

The paper creates synthetic single-cell gene expression data using thermodynamic models to better understand and optimize MPRA experiments for regulatory architecture inference.

This study links modular restructuring in pancreatic cancer signaling networks to treatment efficacy by showing how mutation locations affect disease aggression and control.

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.

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