Gene Regulatory Network Analysis

The paper models bacterial cell size dynamics and protein production strategies to predict correlations between protein concentration and cell size.

This paper designs a synthetic gene circuit to study reaction-diffusion patterning in bacteria, showing how gene network topology influences stripe formation.

This paper develops a framework combining stochastic simulations and machine learning to create predictive models of gene network phenotypes, showing improved accuracy over analytical methods in complex systems.

The paper introduces two new methods for detecting and isolating mass balance errors in reaction-based models, improving error isolation compared to existing approaches.

The paper shows that protein timecourses in yeast can be predicted from mRNA trajectories using a gene-specific model, revealing strain-specific differences in post-translational processes.

This study quantifies information transfer fidelity in gene expression models, showing transcriptional control has higher capacity than translational control, and feedback reduces overall capacity.

The paper constructs a gene regulatory network model for EMT hybrid states in mouse embryonic skin cells using scRNA-seq data and literature-based interactions, identifying Wnt signaling as a key pathway.

The paper quantifies transcriptional noise in E. coli across growth conditions and shows that noise is shaped by regulatory propagation from transcription factors.

This study investigates transcriptional bursting kinetics in mESCs using scRNA-seq and identifies regulatory factors and pathways influencing gene expression noise.

This study analyzes a stochastic genetic circuit with negative feedback and time delays, showing that stochastic delays can reduce protein noise under certain conditions.

The paper evaluates simplification strategies for linear pathway models, showing that truncating pathways leads to inaccuracies and proposing an alternative approach with better performance.

The paper explores the design space of recombinase-based logic circuits by generating and analyzing millions of designs to identify effective architectures for implementing logic functions.

The paper identifies an epigenetic timer mechanism that controls temporal scaling in developmental gene networks, enabling flexible size control through delayed activation of key regulators.

The paper introduces a thermodynamic framework to analyze cellular network behavior using a multidimensional landscape model and applies it to understand heat dissipation and network control.

The paper shows that negative autoregulation of MrpC is essential for synchronized development in Myxococcus xanthus, as its disruption causes asynchronous cell behavior during fruiting body formation.

This paper presents a method to dynamically characterize synthetic gene circuits using time-series data and parameter inference, enabling scalable analysis of complex circuits in E. coli.

The paper demonstrates a method to engineer genetic circuits in synthetic minimal cells, enabling differentiation into new lineages through programmable mating or small molecule stimuli.

The paper uses CRISPR perturbations and single-cell transcriptomics to identify gene regulators and interactions in CD4+ T cells, revealing 134 regulators and 194 regulator pairs.

The paper examines the relationship between topological features of the transcription factor basal regulatory network and phenotypes in humans and yeast, finding correlations with cancer and genetic diseases.

The paper compares formalisms for modeling signal transduction in networks and introduces a new approach using topological factories.

The paper shows that small intracellular and large intercellular variations enhance information transmission, enabling precise tissue function control.

The paper designs and analyzes a layered feedback controller in synthetic biology, showing it combines the benefits of cis and trans feedback mechanisms.

The paper demonstrates a synthetic two-member bacterial community that reaches density and composition steady states controlled by inducer inputs, but shows instability after perturbation.

The paper analyzes how different feedback mechanisms affect protein noise levels in gene expression using a stochastic model and identifies optimal strategies under varying conditions.

This paper uses a convolutional neural network to classify transcriptional regulators in Arabidopsis gene expression data, finding that classifications are based on positive expression correlations.

This paper introduces GRN-loop, a method combining 3D chromatin architecture with gene expression data to identify key transcription factors and DNA elements driving neural lineage commitment.

The paper introduces MERIDIAN, a Maximum Entropy framework to infer signaling network heterogeneity from cell-to-cell variability in network species abundances, and applies it to EGF-induced Akt signaling.

The paper uses a logic-based approach to analyze gene expression differences in breast cancer subtypes, identifying pathway crosstalk between TNF, TGFB1, and EGF in basal-like breast cancer.

The paper introduces an integrated computational pipeline for inferring gene regulatory networks in single cells without prior knowledge, improving accuracy by combining data dimensions and visualizing gene interactions across developmental stages.

The paper develops optogenetic circuits with negative feedback to reduce gene expression noise and control gene activity in mammalian cells.