Gene Regulatory Network Analysis

The paper develops a computational simulator to study how intracellular regulatory signals propagate from single cells to bulk tissue expression data, finding that such propagation is rare and conditions are highly specific.

The paper introduces the quantitative omnigenic model (QOM) to improve GWAS interpretability by integrating regulatory network knowledge, showing it can predict gene expression with fewer parameters and extract causal effect chains.

The paper develops a workflow to reconstruct dynamic transcriptional regulatory networks from public gene expression data using Bacillus subtilis as a case study.

This paper introduces dual-function and multi-level logic gates based on CRISPRi for efficient synthetic circuit design and validation.

This study compares gene regulatory network methodologies to identify biomarker transitions in lung cancer by analyzing regulatory rewiring in gene expression datasets.

The paper introduces CRIBOS, a cell-free genetic circuit platform for multiplex Boolean logic, demonstrating environmental sensing and long-term DNA memory storage.

This paper introduces a kernel filter for adaptive control in biological systems, showing improved performance over particle filters in parameter estimation and control accuracy.

This paper introduces a gene activation system using single-stranded DNA signals in compartmentalized droplets, enabling precise control of gene expression through orthogonal promoters and logic-gate operations.

NetID identifies lineage-specific gene regulatory networks from scRNA-seq data by improving GRN inference through metacell-based analysis and avoids spurious correlations.

This paper identifies critical methodological flaws in the CellOracle approach for gene regulatory network inference and perturbation analysis.

The paper investigates how reducing the switching cost of burdensome gene circuits in E. coli improves population homogeneity and productivity.

This study uses CRISPR perturbations in CD4+ T cells to infer a gene regulatory network, revealing trans-regulatory mechanisms linking immune GWAS genes to signaling pathways.

This paper designs a biomolecular adaptive controller using an IFFL-like topology to restore sustained oscillations in systems that have lost periodicity.

This paper introduces INSCRIBE, a method to reconstruct single-cell signaling history using imaging, and finds that BMP pathway response is heritable across cell divisions.

This paper introduces RegDiffusion, a diffusion model for gene regulatory network inference, showing superior performance on single-cell datasets.

This paper introduces Energy Aware Technology Mapping to design genetic logic circuits with energy efficiency and functionality, showing energy efficiency improves by 37.2% over functionally optimized variants.

The paper investigates how cell state velocity influences quiescence-proliferation fate decisions in breast cancer under hypoxia, finding that inertia, not initial position, determines cell fate.

This paper learns stochastic reaction-diffusion models from limited and noisy data using spatiotemporal features and sparsity enforcement.

The paper introduces an R package for modelling time course data using the retarded transient function approach, enabling analysis of experimental and ODE model trajectories.

This paper introduces biobalm, a tool for constructing succession diagrams of Boolean networks, showing improved performance and demonstrating that non-random models have complex attractor landscapes.

This study introduces STAMP, a novel method for genetic perturbation outcome prediction that improves accuracy across three subtasks and provides a benchmarking framework for evaluating models.

The paper investigates the relationship between binding statistics and Shannon information in cooperative biological networks using two models.

The paper identifies conditions for intrinsic phenotypic oscillations in bacterial cultures without genetic oscillators or external forcing, validated in Bacillus subtilis.

This paper designs sigma factor-based biomolecular classifiers and investigates how resource competition affects their performance and design principles.

This paper introduces an optogenetic clock system that can synchronize populations of engineered cells using light inputs.

The paper examines how the BMP-Smad pathway balances trade-offs among response speed, noise amplification, and sensitivity to receptor input by tuning non-conserved parameters.

LM-Merger merges logical gene regulatory network models to improve coverage and predictive accuracy in disease contexts.

The paper introduces a generalized adder mechanism for cell size homeostasis and shows how it affects clonal proliferation dynamics across different growth laws.

The paper models chemical reaction networks in a random environment using Hawkes processes to approximate marginal descriptions of the system.

This paper introduces a perturbation method to refine Boolean models of cell cycle regulation in budding yeast and mammalian cells by optimizing network dynamics.