Gene Regulatory Network Analysis

This paper defines new theoretical concepts of sloppiness, develops a method to assess model goodness, and applies it to HIV pharmacokinetic models to identify biologically relevant parameters.

This paper improves gene regulatory network reconstruction by combining a new algorithm with a k-nearest neighbor mutual information estimator, achieving better precision-recall performance on synthetic datasets.

This paper compares three reprogramming approaches by modeling the Oct4 gene regulatory network and finds that overexpressing eraser enzymes accelerates and stabilizes reprogramming, with TET outperforming JMJD2 under specific conditions.

McSNAC approximates first-order signaling networks from mass cytometry data and shows accuracy in reconstructing models and predicting perturbation outcomes.

Sensbio identifies potential TF-ligand pairs by comparing input molecules to a TF-ligand database and using a predictive model.

This study investigates the chassis-effect in genetic switches across bacterial hosts, finding that physiological similarity correlates with toggle switch performance.

The paper evaluates gene regulatory network inference methods by analyzing the impact of network structure and data quality on performance, finding that regulatory interaction methods outperform co-expression methods in global inference.

This paper develops a computational method to infer intercellular gene regulatory networks between different cell types using fluorescence reporter data and validates it through in silico case studies.

This paper simulates molecular cell states over time to study gene and protein expression dynamics and their regulatory relationships.

This study shows that using plasmids with identical origins of replication in synthetic bacterial systems can enable population adaptation through copy number coupling and memory.

This paper designs an incoherent merger network in E. coli to achieve a ratiometric gene expression response that is linear, tunable, and robust to cellular perturbations.

This paper develops a systems-theoretic framework to identify robust adaptation mechanisms in biological networks under deterministic and stochastic conditions.

The paper models cell cycle oscillations in Xenopus laevis embryos, showing how bistable switches affect oscillation properties and confer robustness.

This paper introduces spectral methods for quantifying prediction uncertainty in systems biology models using a probabilistic framework, demonstrating their effectiveness in high-dimensional and complex scenarios.

The paper describes a split luciferase assay to measure transcription factor-DNA interactions and uses the data to design synthetic plant promoters with controllable response strengths.

This paper designs protein-based bandpass filters that respond to chemical concentrations, showing OFF-ON-OFF regulation and controlling cellular signaling pathways.

The paper analyzes the bistability characteristics of mutual inhibition, mutual activation, and self-activation motifs using ODE simulations and explores their utility in coordinated gene expression.

This paper develops a pipeline called SPADAN to construct genome-scale dynamic models from time-course experimental data, enabling quantitative predictions of cellular behavior.

The paper presents a pipeline for modeling and parameter identification in biological systems, applied to characterize integrase and excisionase activity in cell-free systems.

This paper describes a miRNA-responsive genetic circuit system that enables real-time monitoring of miRNA expression and functional gene regulation in living cells and animals.

This paper develops a novel MDN modelling technique to study how heterogeneity influences adaptive resistance to CDK4/6 and estrogen receptor inhibitors through protein signalling dynamics.

This work improves genetic design automation by incorporating context effects through thermodynamic models and branch-and-bound techniques, enhancing circuit prediction accuracy.

The paper constructs a Boolean network model of C. elegans developmental pathways to identify causal relationships determining dauer fate based on environmental signals.

The paper analyzes how changes in model parameters affect optimal control policies, time to absorption, and mixing times in Boolean models of biological systems.

The paper identifies a core topological circuit for necroptosis BEC dynamics and demonstrates its robustness through analysis of possible circuit topologies.

This study investigates the feasibility of inferring gene regulatory networks under different noise mechanisms and shows that extrinsic noise improves inference accuracy compared to intrinsic noise.

This study evaluates topological motifs and their coupling in gene circuits using state distributions to identify regulatory interactions involved in human neuron differentiation.

The paper develops a deep learning approach to identify simple causal relationships in gene expression by finding low-dimensional representations that predict genetic perturbation responses.

This paper presents a workflow to calibrate dynamic models using cross-sectional data through Bayesian updating, demonstrated on a puberty model with hormone measurements.