Gene Regulatory Network Analysis

The paper analyzes the accuracy of decoding dynamic NFkB signaling patterns through a mathematical model and shows that optimal decoding parameters can reduce information loss due to biochemical noise.

The paper presents an analytic tool to distinguish pathway-specific variability from environmental variability in single-cell signaling data using synthetic noisy perturbations.

The paper introduces a method to characterize gene circuit dynamics using phase space and shows that expression rates are context-dependent but follow piecewise linear dynamics.

This paper enhances Boolean networks with continuous logical operators and tunable edges to improve qualitative modeling of biological networks without requiring quantitative data.

This paper shows that multicellular incoherent feedforward loops (mIFFLs) can generate spatial patterns, such as edge detection and the 7 stripe pattern in D. melanogaster embryos.

This study analyzes proportional and derivative-based feedback controllers to suppress protein count fluctuations in gene expression, showing their effectiveness and trade-offs in noise suppression and sensitivity.

This paper designs gene circuits to control spatiotemporal growth phenotype patterns in microbial colonies, demonstrating oscillations between growth and dormancy and enabling selective gene expression in non-dividing cells.

The paper models chemotactic behavior in E. coli using a hierarchical approach, showing the behavior can be reduced to a non-homogeneous Markov process.

This study develops ConSReg to predict condition-specific gene regulatory networks in Arabidopsis using genomic data, showing improved performance with ATAC-seq and identifying key regulators in root cell types.

The paper develops two algorithms to compute stationary distributions in non-Markovian stochastic models of cellular processes, demonstrating their application to gene expression and toggle switch models.

The paper extends previous work by analyzing the time-evolution of probability mass functions in gene regulatory networks with slow promoter kinetics.

The paper shows that high gene dosages alter transcriptional regulation of genetic structures, leading to monoallelic expression and cell population heterogeneity, with implications for synthetic biology.

The paper introduces Molecular Process Diagrams to visually represent rule-based models with precision and clarity, extending SBGN standards for better pathway understanding.

The paper designs and tests a synthetic sRNA-based feedback filter module that reduces noise and tunes feedback strength through mathematical modeling and experimental validation.

The paper introduces SBOL-OWL, an ontology that enables machine-readable semantic representation of synthetic genetic circuits, enhancing computational tools' ability to understand and validate designs.

The paper introduces BioLogic, a parallel approach to cell-based logic gates, demonstrating a 3-bit full adder and subtractor in E. coli.

The paper develops a framework linking protein-protein interaction strength to information transmission and dynamical response in signaling networks, finding a speed-information trade-off.

This paper provides design guidelines for stable and effective sequestration feedback networks in synthetic biology.

The paper demonstrates self-organized critical control of genome expression at the single-cell level and proposes a model for cell-fate decision through critical transitions.

The paper analyzes sequestration feedback systems using control theory to identify performance limits and tradeoffs in biological feedback mechanisms.

The paper models microbial consortia using network topology to show how communication structure affects population control and proposes strategies for robust consortium engineering.

The paper presents an optimized Gillespie algorithm implementation for simulating large biological networks with delayed reactions and provides tools for automated code generation.

The paper infers a gene regulatory network from sparse data that controls mouse cortical neurogenesis and generates distinct cortical areas through gene modulation.

The paper analyzes stochasticity in multisite phosphorylation networks using linear noise approximation and proposes an efficient simulation method based on quasi steady state assumptions.

The paper presents a method to qualitatively model signaling networks by integrating experimental data and protein connectivity, showing that senescent cells are less responsive to insulin and activate senescence pathways.

The paper predicts cell-cycle expressed genes in S. cerevisiae using regulatory data, identifying both canonical and non-canonical regulators of time-specific expression.

The paper develops a genome-scale regulatory network for Arabidopsis seed development, identifies key transcription factors, and provides a webtool for analyzing transcriptomes and predicting TF regulatory effects.

The paper shows that cell cycle length correlations in lineage trees reveal growth's role in controlling proliferation, identifying cell size and division license as key factors.

This paper outlines an approach for parameterizing and validating systems biology models to create predictive tools that generate testable hypotheses and expand biological understanding.

The paper uses Bayesian inference to assess context dependence of parameters in an E. coli growth control circuit across different experimental conditions.