Gene Regulatory Network Analysis

The paper uses mathematical models to show that joint regulation of growth and division timing leads to optimal cell size in animal cells by maximizing cellular fitness.

This study uses dynamical models to examine scaffold-mediated signaling and finds that scaffold function depends on assembly paradigms, with implications for therapeutic targeting and synthetic biology.

TRuML translates between Kappa and BNGL rule-based modeling languages to enable interoperability between different software suites.

PyBoost and HiBoost are parallelized Python implementations of GeneClass for learning gene regulatory networks from paired transcription factor and motif data, applied to regeneration and differentiation studies.

The paper compares mutual information estimation methods for transcriptional regulatory network inference using synthetic and real datasets, finding the B-spline estimator performs best and N1/3 bins improve accuracy.

The paper develops new methods for bifurcation analysis of metabolic pathways, applying them to yeast glycolysis to explore steady-state behavior and bistability.

The paper develops methods to infer dynamic gene regulatory network rewiring during EMT from single-cell data and validates their predictive power for drug effects.

This paper presents scalable frameworks for designing multicellular consortia that perform Boolean and history-dependent logic using recombinase-based systems, without requiring cell-cell communication or spatial separation.

This paper develops a system for autonomous control of heterologous gene expression in batch cultures using endogenous promoters and recombinase switches.

This paper reviews the field of reverse-engineering biological networks, discussing methodologies, applications, and the justification for large-scale computational approaches in systems biology.

The paper shows that yeast adapts to new environments through transcriptional tuning at gene promoters, influenced by chromatin modifications.

NetREX constructs context-specific regulatory networks by integrating expression data and context-agnostic networks, showing improved biological consistency in Drosophila data.

The paper investigates how intracellular coupling improves the reliability of cell responses to noisy signals by drawing parallels with information theory.

The paper clarifies the relationship between dynamical compensation and parameter identifiability by defining DC in terms of parameter identifiability across different system states and measurements.

The paper introduces post-inference methods to incorporate prior knowledge into gene regulatory network inference, improving accuracy and removing incorrect interactions.

RegCyanoDB predicts regulatory interactions in 30 cyanobacterial strains using E. coli interactions and the RBH method, resulting in 20,280 interactions with confidence levels.

This paper introduces a rule-based modeling approach using wildcards to simplify biochemical simulations by reducing the combinatorial explosion of species and reactions.

The paper designs a pulsatile communication circuit in bacteria to enable dynamic patterning and long-distance communication through model-driven optimization of promoter and RBS strengths.

The paper develops a two-state stochastic model for gene transcription in Drosophila embryos and shows it can produce bursts of varying sizes and durations, matching experimental data on eve gene stripe two formation.

The paper analyzes a synthetic gene circuit that reduces gene expression noise while controlling mean expression using quorum sensing and negative feedback, validated by simulations and experiments.

The paper develops a stochastic framework to model cell size histories in growing populations and shows that population growth rates exceed individual cell growth rates.

The paper engineers a synthetic developmental gene network in E. coli that couples stochastic differentiation with programmed cell death to enable cooperative cellulose utilization, but also observes cheater phenotypes and models evolutionary escape.

The paper analyzes the evolution of the p53 network across species, showing vertebrate networks enable more complex cell fate decisions than ancestral networks.

The paper develops a stochastic model to analyze noise in cell size control and derives formulas showing how size fluctuations depend on division rate parameters.

The paper identifies 50 genes that control cell-to-cell variability in the yeast pheromone response through a high-throughput screen.

This study shows that compositional context in synthetic gene networks significantly affects gene expression levels and key regulatory properties.

The paper analyzes how different regulatory modes affect gene regulatory motifs' response to input changes, finding that protein-mediated motifs adapt better and feedforward motifs show perfect adaptation.

The paper develops a Bayesian method to infer chemical signal timing and amplitude from population-level response distributions in a temporal logic gate system in E. coli.

The paper constructs a thermodynamically realistic model of the fission yeast G2/M transition, showing that phosphorylation energy controls Cdc2 activation dynamics and bistability.