Gene Regulatory Network Analysis

Closed-loop Optogenetic Control in a Microplate Reader

This paper introduces LEMOS, a low-cost microplate reader for optogenetic feedback control in batch cultures, demonstrating closed-loop gene expression tracking and growth-aware controller design.

A novel reaction-diffusion architecture for engineering self-organized patterns in mammalian cells

This paper introduces a novel reaction-diffusion architecture called JAPI for engineering self-organized patterns in mammalian cells, demonstrating its ability to generate spatial patterns with fewer parameters than classical diffusion-based systems.

Engineered microRNA feedback circuits enable tunable and autonomous control of synthetic receptor activity

This paper introduces a miRNA feedback circuit to control synthetic receptor activity, showing tunable and autonomous regulation of synNotch receptor expression.

Combinatorial transcription factor interactions drive modular gene regulatory networks

The paper uses single-cell overexpression screens to study how combinations of transcription factors drive gene regulatory networks and cell-type specific gene expression programs.

Evaluating valid parameter regimes for biocircuits

The paper evaluates parameter regimes for biocircuits using the Realizability Index (R-index) to quantify valid regions and demonstrates its application to kinetic models.

Benchmarking Static Gene Regulatory Network Reconstruction and Dynamic Transition Probing in Single-Cell Foundation Models.

This study benchmarks static gene regulatory network reconstruction and dynamic transition probing in single-cell foundation models across multiple datasets and reference networks.

Computational screen of promoter configurations that robustly sense transcription factor dynamics

The paper computationally screens promoter models to identify configurations that robustly respond to sustained or pulsatile transcription factor dynamics.

Quantitative Engineering and Investigation of Synthetic Sponge RNAs in E. coli

This study designs, builds, and quantitatively characterizes synthetic sponge RNAs in E. coli, revealing their ability to tune gene expression and provide a quantitative framework for spRNA-mediated regulation.

From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation

Fitting a linear ODE with an equilibrium term via its matrix-exponential solution, not finite differences, raises TF-network AUROC over competing methods on simulated and scRNA-seq data.

Mantis-Delta: Mass-Action Network Theory and Steady-State Characterization for Chemical Reaction Networks

The paper introduces mantis-delta, a Python library that applies CRNT to analyze chemical reaction networks, certifying steady-state properties and solving ODEs for complex systems.

Laplacian Dynamics and Kron Reduction in Species-Reaction Graphs of Chemical Reaction Networks

The paper introduces a method using Laplacian matrices and Kron reduction to simplify and analyze chemical reaction networks by modeling them as species-reaction graphs.

Discovering conserved regulatory modules in predicted gene regulatory networks across species

This paper introduces a relaxed topological alignment algorithm to identify conserved regulatory modules across species' gene regulatory networks, demonstrating improved results over strict alignment methods.

Growth control as a central regulator for tuning the cellular context

This paper introduces a method to control cellular context through tunable growth regulation, enabling precise tuning of genetic circuit behavior and classification of circuit responses.

Physics-Informed Neural Networks for Parameter Recovery in the Repressilator Oscillatory Model

This study evaluates the reliability of physics-informed neural networks (PINNs) for parameter recovery in the repressilator model, showing accurate trajectory reconstruction but challenges in parameter estimation due to noise and model assumptions.

Decoupling between activation time and steady-state level in input-output responses

The paper investigates mechanisms for output decoupling in molecular systems where steady-state levels change with input concentration while activation times remain constant.

PSoup: an R package for simulating biological networks from a qualitative perspective

PSoup is an R package that converts knowledge graphs into qualitative, parameter-free models to predict biological network behavior under perturbations.

Global quantification of mammalian gene expression noise

The paper quantifies gene expression noise in mammalian cells using single-cell proteomics and finds that noise is largely extrinsic and structured across cellular states.

BRIDGE-GRN: Role-Aware Bi-Tower Graph Learning with Cross-View Contrast for Directed Gene Regulatory Network Inference

BRIDGE-GRN improves directed gene regulatory network inference from scRNA-seq data by using role-aware graph learning and cross-view contrastive regularization, achieving strong performance across multiple benchmarks.

Quantitative measurement of synthetic repression curves reveals design challenges for genetic circuit engineering under growth arrest

The paper measures repression curves of genetic NOT gates in E. coli under growth and arrest conditions, finding growth arrest reduces unrepressed expression by >100-fold.

Rigorous Quantitative Analysis of Nonlinear Uncertain Biomolecular Systems using Validated Methods

The paper develops interval analysis methods to rigorously bound steady states in nonlinear biomolecular systems and evaluates their performance on gene network models.

RegVelo: gene-regulatory-informed dynamics of single cells

RegVelo integrates gene regulatory networks with dynamic modeling to predict cellular changes and perturbations, showing improved performance in cell cycle, hematopoiesis, and pancreatic development datasets.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

The differentiation of myeloid progenitors is effected by cascading waves of coordinated gene expression that remodel cellular physiology in a characteristic sequence

The paper profiles gene expression during myeloid differentiation at high temporal resolution, revealing cascading waves of gene regulation that remodel cellular physiology in a characteristic sequence.

Homeostatic feedback model of energy metabolism with adaptive enzyme levels exhibits problem solving behavior

The paper develops a metabolic model to test if adaptive enzyme regulation enables improved recovery after repeated perturbations and how environmental variability affects metabolic plasticity.

A Linear Mixed Effects Model for Evaluating Synthetic Gene Circuits

The paper introduces a linear mixed effects model to statistically evaluate synthetic gene circuits, finding that the fixed effect β describes gate behavior and can be used to assess performance.

COCOA.jl: A Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks

COCOA.jl identifies concordant and kinetic modules in biochemical networks, enabling analysis of concentration robustness with improved scalability.

Noise analysis of derivative-action biomolecular topologies

This study analyzes noise in biomolecular topologies that implement derivative-action signal differentiation, showing that noise amplification can be avoided.

Efficient Stochastic Trace Generation for Transcription

The paper introduces a unified SDE framework for efficient stochastic transcription trace generation, showing high accuracy with reduced computational cost.

Cell Growth and Division Shape mRNA-Protein Correlations

The paper re-examines mRNA-protein correlations in E. coli, develops a theoretical framework accounting for cell growth and division, and validates it with simulations and data.

PEPR-GNN: Perturbation-Enhancer-Promoter-RNA Graph Neural Networks for Multiome Perturb-Seq modeling of regulomes

The paper introduces PEPR-GNN, a graph neural network framework to model regulome responses during genetic perturbations using Multiome Perturb-Seq data.