Gene Regulatory Network Analysis

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.

Multioviz enables in silico perturbation and analysis of gene regulatory networks to guide experimental decisions using a real dataset from mice.

This paper shows that noise-exploiting controllers can effectively manage multi-cell systems with time delays and model uncertainties, enabling distinct control of multiple cells with a single input.

This paper presents three biomolecular anti-windup topologies to prevent instability in integral controllers by linking control-theoretic strategies to chemical reaction networks.

The paper develops a modular protein secretion platform with tunable logic and control for synthetic biology applications in cell therapies.

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 introduces a logical directed graph framework for analyzing biological regulatory networks and applies it to study a mollusk neural network.

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.

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.

CellPolaris uses transfer learning to generate cell-type-specific gene regulatory networks from transcriptomic data, achieving state-of-the-art results in predicting regulatory factors and simulating cell fate transitions.

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 develops a deep learning model to estimate gene regulatory networks and RNA kinetics from single-cell RNA-seq data, showing improved recovery of known regulatory relationships and predictive power.

The paper introduces the CASwitch, a synthetic gene circuit that reduces leakiness while maintaining high inducible gene expression using mathematical modeling and experimental validation.

The paper develops an analytical model for noise propagation in gene expression cascades, showing how cascade structure, length, and other factors influence intrinsic and global noise.

This paper analyzes the frequency response and sensitivity of biochemical cascades with varying phosphorylation layers and states.

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

The paper investigates size regulation mechanisms in slow-growing E. coli cells, finding that different models explain size control under varying media conditions.

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.

The paper investigates the trade-off between upstream and downstream noise in signaling networks, showing that longer timescales buffer upstream noise but accumulate downstream noise, confirmed in biological systems.

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.

The paper introduces MC-Boomer, a method using Monte Carlo Tree Search to generate Boolean models consistent with experimental data, revealing novel biological mechanisms.

The paper introduces scMeMo, a mechanistic modeling framework that infers unobserved variables in single cells using diverse measurements, demonstrating its ability to predict protein levels and cell cycle behavior from limited data.

This paper introduces RENGE, a method for inferring gene regulatory networks from time-series scCRISPR data, which outperforms existing methods and produces GRNs consistent with known databases.