Gene Regulatory Network Analysis

This study uses a mathematical framework to analyze how stochasticity and nonlinearity influence gene regulatory network motifs, showing how fluctuations affect cell fate decisions.

This paper introduces an END-nSDE framework to reconstruct noisy gene regulation dynamics from heterogeneous cell populations, outperforming RNNs and LSTMs in modeling extrinsic noise effects.

This paper introduces a method using Chatterjee's rank correlation to construct gene regulatory networks from scRNA-seq data, outperforming existing methods in capturing complex dependencies.

This study derives exact switching time distributions for a stochastic model of autoregulatory gene expression, showing that explicit mRNA dynamics lead to new distribution shapes not captured by reduced models.

This study evaluates methods for inferring Jacobian matrices from single-cell data to model gene regulatory dynamics, finding that while some methods capture behavioral properties, they fail to reliably capture structural properties.

This paper analyzes synchronous and asynchronous update schemes in Boolean networks and shows that the canonical form can only be obtained with the synchronous scheme.

This paper benchmarks four Boolean network optimization methods for gene regulatory modeling, showing distinct performance in accuracy, efficiency, and robustness to perturbations.

RealTrace is a Bayesian method that removes measurement noise from time-lapse microscopy data to reveal biological dynamics, showing fluctuating growth rates and gene expression in single cells.

The paper develops an analytical method to compute mRNA and protein count distributions under random cell division times and analyzes noise contributions from intrinsic and extrinsic sources.

BayesCNet improves inference of cell type-specific regulatory networks by leveraging cell type hierarchy, outperforming existing methods, especially for rare cell types.

This study introduces a CRISPRi-based controller to mitigate Winner-Take-All behavior in synthetic gene circuits by promoting balanced module activation through negative feedback.

The paper introduces an AI method to erase memories in gene regulatory networks without altering their structure, with implications for biomedical applications and evolutionary theory.

This paper designs synthetic phosphosignaling circuits in bacteria using two histidine kinases and a shared response regulator to enable multi-input signal integration and logic gate behaviors.

The paper introduces a recombinase-based control strategy to reduce resource competition in synthetic gene circuits, demonstrating its effectiveness through computational and experimental methods.

DART uses deep learning to infer promoter states from live-cell imaging data, revealing a linear coupling between activation and inactivation rates.

The paper simulates gene regulatory networks and models perturbation effects to understand network structure and function.

The paper investigates how Snail-mediated repression coordinates cell fate decisions through transcription factor cooperativity and multiple inactive promoter states during development.

PEtab.jl improves dynamic modelling efficiency by comparing ODE solvers and parameter estimation methods, showing faster performance than pyPESTO for medium-sized models.

This paper evaluates Universal Differential Equations (UDEs) for systems biology, showing their versatility but highlighting challenges with noise, data scarcity, and interpretability.

The paper develops a flexible modeling approach that balances interpretability and accuracy, showing a simple signaling model can explain T cell frequency responses to pulsatile stimuli.

This study examines how negative feedback and cell-cell signaling interact to regulate noise at single-cell and population levels in a quorum sensing system.

This paper introduces a communication-free method to stabilize microbial consortia using stochastic phase variation, demonstrating dynamic stability in 2- and 3-state consortia.

This tutorial demonstrates how to use the CoLoMoTo software suite to perform reproducible dynamical analyses of logical models of biological networks.

The paper analyzes the AC-DC genetic circuit to show it can exhibit diverse multifunctional dynamics, including coexisting oscillations and multistability, challenging traditional views of network constraints.

This study shows that periodic miRNA synthesis can more effectively repress target RNA than constant synthesis, with optimal frequency-dependent repression.

STARNet infers gene regulatory networks from spatial multi-omics data, revealing domain-specific regulatory interactions in tissues.

chronODE integrates time-series multi-omics data using ordinary differential equations and machine learning to model gene and chromatin dynamics during brain development.

The paper introduces a three-state gene expression model to analyze single-cell multi-omics data, revealing distinct gene expression strategies through parameter inference.

The paper introduces GRouNdGAN-Toolkit, a toolkit for simulating scRNA-seq data and benchmarking GRN inference methods using causal generative models.

This paper presents a genetic circuit that dynamically regulates circuit gain by controlling plasmid copy numbers in E. coli, validated through mathematical modeling and experimental testing.