Gene Regulatory Network Analysis

The paper investigates factors affecting genetic circuit performance and develops a model to predict performance under untested conditions.

This paper uses optimal control theory to model morphogen-driven tissue patterning, showing how cells can achieve precise and reproducible fate decisions despite noise and dynamic feedback.

FitMultiCell provides a scalable pipeline for modeling, simulating, and parameterizing multi-scale multi-cellular processes using Morpheus and pyABC.

This paper investigates the Poisson representation linking discrete and continuous models of stochastic gene regulatory networks, showing it holds for unregulated genes and approximates for regulated ones in large protein number limits.

This paper develops a theory to quantify and correct bias in transcriptional parameter inference from single-cell data by analyzing the effects of extrinsic noise.

The paper constructs and analyzes gene regulatory networks for breast cancer subtypes, adjacent normal tissues, and normal tissues to identify subtype-specific and pre-cancerous regulatory features and candidate genes.

The paper evaluates gene regulatory network prediction methods in early human embryos, finding that MICA, combining mutual information with chromatin accessibility, provides more reproducible and biologically meaningful results.

ReDirection assigns reaction outcomes and dissociation constants to user-defined biochemical networks using a numerical method based on stoichiometry matrices.

The paper introduces PaLD, a method for analyzing time course transcriptomic data, and shows it identifies gene regulatory network structures not found by other methods.

The paper develops a method to automatically discover structurally identifiable and interpretable dynamical models from biological data using SINDy-PI and additional procedures.

Catalyst introduces a Julia library for modeling and simulating chemical reaction networks with high performance and flexibility.

This study models macrophage phenotype transitions using a stochastic gene-regulatory network, identifying bistable and tristable states and their transition dynamics.

The paper assesses whether gene regulatory networks can predict gene sensitivity in cancer cell lines and finds that mRNA abundance is a better predictor than inferred regulatory activity.

This paper introduces a Bayesian model to infer transcription factor activity from gene expression data using logical rules and applies it to bulk and single-cell transcriptomics to study fibroblast plasticity.

The paper designs and analyzes transcription factor-based amplifiers to improve temporal control in synthetic circuits, enhancing self-organization and tumor-killing capabilities of CAR T cells.

MINI-AC infers plant gene regulatory networks using bulk or single-cell accessible chromatin data, outperforming existing methods in identifying TF binding sites and revealing cell-type specific regulators.

The paper explores the relationship between structural and functional modularity in gene regulatory networks using Boolean models and simulations, showing that structural decomposition correlates with dynamic behavior.

This paper shows how transcription factor competition can enable self-sustained oscillations in a minimal genetic feedback loop without cooperative binding.

The paper provides a mathematical model of cholesterol regulation pathways, revealing antithetic integral control as the mechanism for robust homeostasis of ER cholesterol.

This study constructs a logic-incorporated gene regulatory network model to analyze how regulatory logic and noise influence cell fate decisions in differentiation and reprogramming.

BioNetGMMFit estimates parameters of BioNetGen models from time-stamped single-cell data and provides confidence intervals for improved accuracy.

CollecTRI-derived signed regulons, spanning 1,183 transcription factors (TFs) and 45,856 TF-gene links, outperform DoRothEA, ChEA3, RegNetwork and Pathway Commons at identifying perturbed TFs in KnockTF data.

The paper develops a statistical method to identify bistability near developmental transitions from gene expression data and applies it to honey bees.

This paper introduces a regularization method for identifying cell-type specific mechanisms in dynamical models, demonstrating its effectiveness with synthetic and biological data.

This study investigates how dynamic interferon-gamma stimulation affects pro-inflammatory gene expression in macrophages, revealing gene-specific response patterns and stochasticity in CXCL10 expression.

The paper studies spatial patterns in synthetic gene networks in E. coli colonies using statistical mechanics models and simulations, finding power-law scaling and anti-correlations.

This paper reviews techniques for controlling Boolean gene regulatory networks, comparing methods like algebraic approaches and feedback vertex sets using a T-LGL leukemia model.

This paper designs a portable regulatory RNA array for tunable gene regulation across diverse bacteria, enabling complex circuitry without requiring large regulatory libraries.

The paper describes how coherent feedback can eliminate background influences in homeostatic systems, showing that oscillatory and non-oscillatory controllers can maintain consistent responses regardless of background levels.