Gene Regulatory Network Analysis

The paper introduces a Monte Carlo method to model and visualize the Waddington's epigenetic landscape of gene regulatory networks, showing how attractors and paths between them represent stability in biological systems.

This study shows that noise in molecular interactions enhances information acquisition in biological signaling pathways, with optimal information acquisition occurring near maximal noise levels.

TENET reconstructs gene regulatory networks from scRNA-seq data using transfer entropy, identifying key regulators with better performance than existing methods.

The paper develops a method for quantitative analysis of fluorescence flow cytometry data in bacteria and introduces an open-source tool for this purpose.

This paper introduces a method to conditionally stabilize proteins by masking a degron tag with an affinity sensor, demonstrating its application in yeast for oncoprotein detection.

The paper evaluates when ensembles of models improve predictions in systems biology and network inference, emphasizing the importance of careful ensemble construction.

This paper proves that ACR systems contain an internal integral structure, enabling robustness to broader disturbances and facilitating modular synthetic biology design.

The paper introduces two computational methods for analyzing complex biological models and applies them to predict T cell responses to checkpoint inhibitors.

The paper demonstrates real-time feedback control of a molecular titration motif in E. coli using microfluidics and validates the approach with in silico simulations.

This paper presents systematic and automated frameworks for implementing robust history-dependent genetic programs in cellular populations.

The paper reconstructs gene regulatory networks from three mouse cell atlases to identify global and cell-type specific regulatory modules and their roles in cellular identity.

The paper uses live single-cell imaging and computational modeling to show that diverse kinetic stimulations improve prediction of signaling dynamics by better constraining model parameters.

This study identifies gene expression burden in mammalian cells and introduces miRNA-based circuits to mitigate it, improving synthetic circuit performance.

The paper designs and evaluates robust controllers for combined transcriptional and translational resource allocation in synthetic circuits, identifying performance trade-offs and promising architectures.

This paper introduces a reduced ODE model that incorporates spatial effects through a binding correction factor (BCF) to better analyze and design genetic circuits in bacteria.

The paper develops a theoretical framework to model transcription bursting by analyzing the stochastic dynamics of transcription rates and their impact on mRNA distribution.

The paper introduces CLASP, an optogenetic tool to control TF localization, and shows differential promoter responses to pulsatile vs. continuous TF inputs.

The paper investigates a network of three mutually repressing master regulators, showing it can lead to multiple stable cell states and applies this to understand T cell differentiation.

FlopR calibrates and normalizes flow cytometry and plate reader data using a unified R package, validated on E. coli GFP expression data to identify subpopulations from bulk measurements.

This paper introduces Most Permissive Boolean Networks (MPBNs) to improve the modeling of biological networks by avoiding spurious behaviors and reducing computational complexity for genome-scale analysis.

The paper infers a gene regulatory network (GRN) from perturbation experiments in cancer cells, identifying both known and novel regulatory interactions that contribute to oncogenic processes.

The paper introduces a mathematical model that integrates DNA methylation with gene regulatory networks to quantify its role in gene silencing stability.

The paper develops a framework to distinguish stable and unstable expression variation in cell populations and applies it to TCR expression in CD4 T cells, finding 70% stable variance in polyclonal populations and over 80% volatile variance in isogenic populations.

The paper constructs synthetic immediate-early genes (synIEGs) to study signal-decoding circuits, demonstrating their ability to implement complex regulation and respond to combined stimuli.

The paper identifies functional elements in plant promoters and develops minimal synthetic promoters for precise gene expression control in plants.

This paper defines and mathematically characterizes multiplexing in signaling systems, provides experimental evidence for NF-κB's potential to multiplex information, and introduces new methods for analyzing dynamic models.

The paper presents a stochastic model of gene expression incorporating polymerase recruitment and pause release, showing it explains experimental data better than the telegraph model and predicts gene dosage compensation.

The paper develops a general model to explore how cell-cycle regulation mechanisms are constrained by cell-size homeostasis measurements across different organisms.

The paper identifies autoregulatory and dual feedback gene network motifs in human tissues, showing their prevalence and conservation, suggesting they drive dynamical gene expression.

The paper demonstrates how the SBOL standard can represent multicellular biological systems, enabling better sharing of complex designs across multiple cells.