Gene Regulatory Network Analysis

This paper introduces a probability generating function-based method for parameter inference in stochastic biochemical reactions and shows it outperforms existing methods in efficiency, accuracy, and robustness.

This study evaluates the Segregostat system's ability to control stress responses in microbial cells under industrial bioreactor conditions using automated flow cytometry.

This paper introduces the Hyper Solution Landscape (HSL) method to analyze cell fate decisions by exploring gene regulatory network configurations and their dynamic changes.

This study optimizes the waiting time between dual-pulse nucleoside labeling pulses to improve signal-to-noise ratio in cell cycle analysis, particularly for experiments with small cell numbers.

The paper provides a quantitative design guideline for positive feedback in biomolecular systems, showing how feedback strength relates to saturation parameters and demonstrating its application in gene regulation models.

GRNFormer infers gene regulatory networks from transcriptomics data using a graph transformer framework, outperforming existing methods and recovering known and novel regulatory interactions.

This paper analyzes how two antagonistic negative feedback loops regulate compensatory responses to environmental changes, identifying three control modes and their implications for homeostasis and rheostasis.

This paper introduces genetic devices for precise control of cell-type ratios and multicellular morphology in synthetic biology systems.

The paper designs synthetic promoters in yeast using neural networks to rewiring transcriptional networks with high repression rates.

SPSID denoises gene regulatory networks by distinguishing direct from indirect interactions, outperforming existing methods in simulations and benchmarks.

This paper uses a generative model to design synthetic gene circuits that achieve specific functional and evolutionary properties.

A hybrid 4D whole-cell simulation of the minimal bacterium JCVI-syn3A integrates spatial reaction-diffusion, metabolism, polymer chromosome dynamics, and division across a complete 105-minute cell cycle.

This paper introduces the Filtered Finite State Projection (FFSP) method to solve high-dimensional stochastic filtering problems in biochemical reaction networks, demonstrating its effectiveness with numerical examples and real biological data.

This paper derives an analytical solution for stochastic gene expression dynamics with mRNA-sRNA interactions and develops a parameter-inference method for efficient kinetic parameter estimation.

This paper combines mechanistic and machine learning models to address resource competition in synthetic biology, demonstrating improved predictions and physical consistency.

This study shows that stochastic growth fluctuations, not cell size or division, drive cell-to-cell growth variability in lymphocytic leukemia cells.

This paper introduces GEMINI, a tool that records cellular activity histories with high temporal resolution, and demonstrates its application in tracking transcriptional changes and inflammation dynamics in vivo.

This paper uses statistical mechanical models to study how endogenous signaling parameters, such as effector concentrations, influence the stability of genetic circuits in living cells, contrasting with traditional approaches that rely on Hill functions and manually tuned parameters.

The paper constructs and tests synthetic circuits in E. coli to show that positive feedback-regulated secondary signals can propagate faster and farther than diffusion-limited signals.

This paper improves gene-regulatory network inference by using epigenomic priors, showing better accuracy with DNA methylation data compared to chromatin accessibility data.

The paper develops a framework to assign context-dependent weights to gene regulations from literature, improving biological predictions and mathematical modeling of disease-specific networks.

This paper develops synthetic gene circuits that selectively target RAS-driven cancers by integrating multiple RAS sensors to enhance selectivity and therapeutic potential.

This paper provides a unified Hilbert space framework for the spectral theory of stochastic gene expression and shows that deterministic models fail to capture relaxation rates under strong autoregulation.

The paper investigates how cell-state differences affect drug responses and signaling network interactions in human epidermal stem cells.

This paper introduces a computational framework for designing mammalian genetic programs using genetic algorithms and dynamical systems models, demonstrating its ability to find optimal circuit designs and validate them experimentally.

This paper designs synthetic genetic circuits to implement non-associative learning behaviors in unicellular and multicellular systems.

This paper introduces a method to predict reversible and irreversible cell state transitions using gene regulatory networks and gene expression data.

This paper introduces a genetic feedback control strategy for microorganisms to self-regulate population density using quorum sensing and an antithetic controller.

The paper models the eukaryotic cell cycle as a tetra-stable excitable system using ODEs, phase plane analysis, and bifurcation theory, showing that two existing frameworks represent the same underlying architecture.

This paper develops a multiscale model, GEAGS, to predict transient oscillations in synthetic gene circuits by coupling gene expression to growth phases, revealing amino acid recycling and growth transitions as key factors.