Gene Regulatory Network Analysis

The paper describes a stable DNA replication mechanism for synthetic genetic circuits and explains its derivation from a mechanistic model of ColE1 replication.

This paper develops a method to monitor multiple gene expressions in single cells using ratiometric RNA labeling and applies it to study synthetic gene circuits.

The paper analyzes single-cell RNA sequencing data using transition path theory to study gene regulation during cell phenotypic transitions, finding increased intercommunity regulation and GRN frustration.

This paper introduces a modeling framework for biological feedback loops that translates molecular-level biochemical principles to organismal-level behavior using analytical and computational methods.

The paper uses single-cell imaging and machine learning to predict cell division from ERK and Akt activity dynamics in MCF10A cells, finding ERK alone predicts division with AUC=0.74.

The paper uses partial information decomposition to analyze information transfer in C1-FFL networks and finds that redundant and synergistic information transfers peak before output gene expression noise increases.

The paper examines how biological cells make fast, risky decisions that contribute to inflammation and cancer, drawing parallels to computational strategies for solving complex problems.

The paper develops PhosTF to infer kinase/phosphatase regulation of transcription factors in yeast, finding that over 70% of interactions involve deactivation.

Regulus infers signed transcriptional regulatory networks from few samples by integrating TF binding, gene expression, and regulatory region data with biological constraints.

CaiNet simulates gene regulatory networks with stochastic molecular kinetics and infers steady-state network structure from measurements.

The paper shows that kinetic network features, not molecule abundances, are more predictive for drug response in a TRAIL cell death model using machine learning.

The paper introduces a parallel framework for genome-scale gene regulatory network inference, enabling efficient inference of large networks without compromising quality.

scSGL infers gene regulatory networks from single-cell RNA sequencing data by learning signed graphs that capture both activating and inhibitory gene relationships, while handling cell-cycle heterogeneity and dropouts.

This study compares synchronization in activator- and repressor-based coupled gene oscillators, showing that repressor-based models achieve robust in-phase synchronization while activator-based models require coupling delays for anti-phase synchronization.

The paper introduces effective deficiency to better assess the capacity of constrained biochemical networks to permit steady state variations, showing it is smaller than classical deficiency.

The paper uses NK models and Boolean logic to predict trait phenotypes from gene network topology in microorganisms through numerical simulations.

The paper uses CRISPRi and single-cell RNA-seq to reverse engineer gene regulatory networks in colorectal cancer cells, revealing autoregulatory and cooperative mechanisms.

The paper predicts drug synergies using a logical modeling framework that combines manual curation with automated parameterization, showing that automated methods can outperform manual models in some cases.

The paper describes engineered bacteria that solve 2X2 maze problems using chemical inputs and genetic logic circuits.

This paper introduces a deep learning framework to solve high-dimensional chemical master equations using reinforcement learning and neural networks, enabling efficient approximation of solutions and their parameter sensitivities.

SHINE improves network structure learning from high-dimensional data with limited samples by leveraging shared structural information across multiple networks.

The paper introduces a Bayesian and machine learning approach to assess predictive uncertainty in mechanistic models of apoptosis using experimental data, showing that non-quantitative data can improve model accuracy when combined.

The paper develops a computational pipeline to evaluate inference accuracy across different model levels and parameter inference settings for negative feedback gene regulation.

The paper infers mechanistic gene networks from single-cell data and shows they outperform neural networks in predicting gene dynamics and knockout effects.

This paper introduces COSLIR, a method to directly reconstruct gene regulatory networks from time-stamped single-cell data without pseudo-time inference, showing good performance on real datasets.

The paper maps the cell cycle period landscape using synthetic cells and identifies polymorphic responses to PP2A inhibition.

This paper describes a cost-effective and rapid method for characterizing genetic parts using cell-free lysates and high-throughput techniques.

AGENT provides a curated GRN tool for Arabidopsis with visualization and analysis features to study gene regulatory networks in flowering and seed development.

The paper develops a whole-colony model of E. coli integrating single-cell mechanisms with population-level interactions, showing that AmpC expression, not AcrAB-TolC, drives antibiotic survival.

The paper shows that budding yeast use a push-pull repression system to dynamically match glucose transporter levels to extracellular glucose concentrations.