Gene Regulatory Network Analysis

TRENDY improves gene regulatory network inference by integrating transformer models into the WENDY method, showing better performance on simulated and experimental data.

The paper investigates how network logic (additive vs multiplicative) influences EMT transitions and shows that AND logic better matches experimental data on miR-200 regulation.

This study analyzes bursty protein production in single cells and expanding populations using a Markovian model with feedback, showing differences in distribution patterns between single-cell and population-level dynamics.

The paper analyzes gene expression dynamics in MCF-7 cells under HRG and EGF stimuli, revealing thermodynamic phase synchronization driven by the critical point (CP) and its role in cell fate transitions.

This study improves genetic circuit function in E. coli by evolving strains in complex environments using adaptive laboratory evolution.

The paper develops a toggle switch genetic circuit in plants using computationally selected components and in silico models, demonstrating predictable function in stably engineered plants.

This paper develops a dual-responsive synthetic gene circuit that combines circadian and inflammatory signals to enable timed and enhanced therapeutic drug delivery in inflammatory arthritis.

MAGELLAN constructs interpretable computational models from pathway data and experimental rules, demonstrating predictive accuracy in breast cancer and NSCLC signalling.

This paper designs and experimentally validates biomolecular neural networks using molecular sequestration, demonstrating their ability to function as linear classifiers in living systems.

The paper uses single cell transcriptomics and mathematical modeling to show how cyclin D, cyclin E, and CDKN sub-modules in the G1/S network drive distinct proliferation trajectories in hematopoietic cells.

OTVelo estimates gene velocities using optimal transport to infer dynamic gene regulatory networks from time-stamped single-cell gene expression data.

This paper improves inverting genetic logic gates by introducing antisense RNA feedback, enhancing their steepness, reducing leakage, and enabling tunable dose-response curves.

The paper shows human cells adapt to methotrexate through stochastic tuning, a process similar to yeast, which is reversible and not mutation-driven.

The paper proposes a dynamic perspective on homeostasis, suggesting it emerges from multi-dimensional attractors rather than explicit control mechanisms, supported by models and single-cell dataset analysis.

The paper identifies three fundamental classes of gene network topologies that can lead to pattern formation through extracellular signaling in multicellular systems.

This study investigates the robustness and dynamics of Min protein oscillations in E. coli, showing their optimal expression levels and complex patterns under varying conditions.

This paper designs a modular genetic circuit that enables pulsatile control of bacterial population dynamics through AHL-based quorum sensing and inducer-regulated transcriptional cascades.

The paper analyzes transcriptional bursting in Drosophila embryos to understand how spatial gene expression patterns are established despite variability in transcriptional activity.

This study enhances synthetic hypoxia biosensors by integrating natural hypoxia-responsive factors and feedback circuits to improve their performance and understanding of hypoxia regulation.

This paper combines gene regulation network models with polygenic prediction to explore how phenotypic variation can be captured using statistical methods, identifying key regulatory connections and linking findings to concepts like global sensitivity and epistasis.

This paper quantifies binding cooperativity between adjacent transcription factors on DNA and identifies its functional roles in gene regulation.

The paper demonstrates how mutations can convert between D-fucose-activatable and -repressible acetyltransferase switches in fusion proteins.

This study shows bacteria adapt to fluctuating environments using power-law memory, linking cellular dynamics to learning-like behavior through a theoretical model and experimental validation.

This paper designs a PIDA control scheme as a chemical reaction network to improve dynamic performance and steady-state tracking in biochemical systems.

The paper shows that growth rate affects the sensitivity of gene regulatory switches in E. coli, with experiments confirming theoretical predictions about inducer concentration thresholds.

The paper analyzes degeneracy in adaptive circuit mechanisms, showing how it leads to adaptation and resonance in response to different input types.

This paper introduces scRegNet, a framework combining single-cell foundation models with graph-based learning to predict gene regulatory links, achieving state-of-the-art results on benchmark datasets.

This paper develops a method for intercellular communication in E. coli using M13 phagemids and CRISPR-based gene regulation to build logic gates for multicellular computing.

The paper uses optogenetic tools to control pheromone gradients in budding yeast, enabling spatial regulation of mating behavior and cell fusion.