Gene Regulatory Network Analysis

Reference Regulatory Element-Guided Gene Expression Analysis for Mechanistic Inference of Gene Regulatory Networks

This paper introduces REGA, a framework that uses reference regulatory elements to infer gene regulatory networks from expression data, showing improved performance across multiple benchmarks and applications.

Assembly-coupled feedback enables robust control of flagellar number

The paper investigates how bacteria control flagellar number through assembly-coupled feedback, showing robustness arises from a balance between intrinsic fluctuations and regulator variability.

An engineered multi-step differentiation program in Escherichia coli for self-organized spatial patterning

This paper engineers a multi-step differentiation program in E. coli to generate self-organized spatial patterns through symmetry breaking, paracrine, and autocrine signaling.

From Lipid Dynamics to Precision Predictions: A New Approach Methodology for Precision Modeling of Phosphoinositide Signaling

This paper develops a new methodology to model phosphoinositide signaling dynamics, showing that basal PI(4)P levels determine cell-specific responses to genetic perturbations.

Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization

This paper introduces Giraffe, a matrix factorization method for gene regulatory network inference that estimates signed regulatory effects, showing improved accuracy in distinguishing activating and inhibitory interactions across multiple biological contexts.

Gene Regulatory Networks that support Multi-Fate Cellular Decisions

This study identifies network structures that support multi-fate cellular decisions by analyzing gene regulatory networks using monotone Boolean models.

Hidden sampling biases inflate performance in gene regulatory network inference

The paper shows that sampling biases in negative regulatory interactions inflate performance in gene regulatory network inference and proposes a degree-aware sampling approach to address this issue.

Impact of competition between precursor and mature microRNAs on stochastic gene expression

The paper investigates how competition between precursor and mature microRNAs affects stochastic gene expression in feedback loops, showing noise-induced bimodality outside deterministic bifurcation regions.

Multimolecular proofreading overcomes the activity-fidelity trade-off

The paper designs a multimolecular proofreading circuit that overcomes the activity-fidelity trade-off in synthetic biological systems.

Gene Regulatory Networks Mediate Pattern Scaling in Growing Tissues

The paper develops a theoretical framework showing gene regulatory networks (GRNs) enable pattern scaling during tissue growth without relying on morphogen scaling.

Robust Multiplicative Control in Chemical Reaction Networks Extended Version

This paper develops a control framework for chemical reaction networks to robustly track multiplicative combinations of species concentrations and validates it with simulations.

Negative Autoregulation Promotes the Evolution of Strong Environmental Switching

This study shows that negative autoregulation in gene circuits enables stronger environmental switching by favoring alleles that create strong molecular switches in E. coli.

Weak form Scientific Machine Learning for Systems Biology: A Tutorial on WENDy

This paper introduces WENDy, a method for parameter estimation in systems biology models using weak-form regression, demonstrating its application on real experimental data for glycolytic oscillators and cell-state transitions.

N-Gene and T-Gene Deregulation Networks: A data-driven causal framework for the analysis of gene interventions in cancer

The paper introduces Gene Deregulation Networks (GDNs) to analyze gene interventions in cancer by constructing networks from gene expression data without prior functional annotations.

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks

The paper identifies distinct geometrical landscapes for two gene regulatory motifs that exhibit tristability and shows how these landscapes can model cell fate transitions in cancer.

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data

The paper introduces CSCN, a method to infer cell-specific causal gene regulatory networks from scRNA-seq data, reducing false positives and improving state clustering.

A Mecom—Cdk6 toggle switch governs hematopoietic stem-to-multipotent fate transitions via distinct multistable landscapes

The paper models the Mecom-Cdk6 regulatory network to show how IGF signaling and self-activation shape stem cell fate transitions through multistable landscapes.

PARROT: Phase-Altering Regulatory Rewiring Over Time

PARROT detects change-points in dynamic gene regulatory networks using Stochastic Block Models, identifying module reassignments and connection changes in human and mouse data.

System Identification and Control for Optogenetics in Mammalian Nucleocytoplasmic Transport

This paper benchmarks predictive modeling and control techniques with optogenetics to steer nucleocytoplasmic transport in mammalian cells, showing robust performance against cell-to-cell variation.

Computational reconstruction of hierarchical cis-regulatory networks reveals synergistic transcription control and disease-associated rewiring

ORIGAMI reconstructs cis-regulatory networks using multi-omics data and deep learning, revealing synergistic transcription control and disease-associated rewiring.

SimMapNet: A Bayesian Framework for Gene Regulatory Network Inference Using Gene Ontology Similarities as External Hint

SimMapNet improves gene regulatory network inference by integrating Gene Ontology similarities, outperforming existing methods on bacterial and fly datasets.

Self-organized traveling waves in a synthetic multicellular reaction-diffusion system

The paper designs a synthetic bacterial system that generates self-organized traveling waves through integrated feedback and diffusible signals, showing how circuit architecture and diffusion influence pattern dynamics.

Expanding gene regulatory networks from transcriptome data through graphical modeling with heterogeneous priors

This paper introduces a method to expand gene regulatory networks by incorporating prior knowledge, outperforming conventional methods in distinguishing true interactions from indirect associations.

Combining prior knowledge and transcriptomics data for logic models of patient subgroups

The paper introduces FICUS, a framework that integrates omics data with logic models to identify shared signaling mechanisms across patient subgroups.

Cell division dynamics generate heterogeneous contact-mediated signaling outputs

The paper models how cell division dynamics influence heterogeneous contact-mediated signaling outputs in growing cell collectives.

Orthogonal Acoustic Control of Gene Expression Using a Synthetic T7RNAP Rapa-Inducible Dimerization System

This paper introduces an ultrasound-responsive gene regulation system in bacteria that uses an AND-gate mechanism combining ultrasound and rapamycin to control gene expression in a microbial consortium.

A general thermodynamic approach for model reduction of enzyme cycles and electrogenic transporters

This paper introduces a thermodynamic framework for reducing enzyme cycle models while preserving physical constraints, applicable to both hypothetical systems and real data like Na+/K+ ATPase.

Multiple Fault Analysis and Drug Therapy on Signaling Pathways Using Dynamic Bayesian Network-based Model

This paper develops a Dynamic Bayesian Network model to analyze multi-fault behavior in signaling pathways and prioritize drug interventions based on efficiency and burden scores.

Varying parameter ranges alters both Partial Rank Correlation Coefficient results and phenomenological behavior when modeling the epithelial mesenchymal transition

This paper examines how varying parameter ranges in a mathematical model of EMT affects PRCC results and the preservation of bistable switch behavior.

Phase separation as a tunable regulator of canonical gene regulatory motifs

This study investigates how phase separation of transcription factors affects the dynamics of gene regulatory motifs, showing it tunes stability and noise in network behavior.