Gene Regulatory Network Analysis

A spin glass representation of hierarchical gene regulation and the partial resolution of frustration

This paper develops a multicellular Ising model to study frustration in gene regulatory networks and shows how intercellular communication affects frustration resolution.

SiSaNA: A Python-based command line interface for Single-Sample Network Analysis

SiSaNA provides a command line interface for single-sample regulatory network analysis without requiring programming experience.

Mechanistic gene-regulation-based trajectory inference with bowside

The paper introduces bowside, a neural model for trajectory inference and gene regulatory network reconstruction in single-cell RNA sequencing data, showing it recovers known regulatory motifs in hematopoiesis and reprogramming.

Event timing precision in stochastic gene expression withbursty production

The paper analyzes how degradation and bursty production affect the optimal feedback for minimizing timing variability in stochastic gene expression.

Core Coefficient quantifies transcription-factor functional dominance for therapeutic target selection

The paper introduces the Core Coefficient (CC) to quantify transcription factor functional dominance in regulatory networks, showing it can predict therapeutic target shifts in disease progression.

Effects of dose-response relationships on cellular information transmission

This study quantifies how dose-response relationships in signaling systems affect information transmission under various noise distributions and identifies design principles for improved signaling fidelity.

SPARK: Bayesian structure learning of enhancer-based gene regulatory networks from single-cell multiomics data and prior knowledge

SPARK reconstructs enhancer-based gene regulatory networks using single-cell multiomics data and prior knowledge, revealing regulatory links associated with immune disease GWAS variants.

A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks

The paper benchmarks clustering strategies for patient-specific gene regulatory networks across 9,778 cancer samples, finding that standard deviation-based feature selection with k-means performs best.

An Efficient Implicit Tau-Leaping Method for Estimating Second-Order Sensitivities of Stiff Stochastic Biochemical Systems

The paper introduces an efficient implicit tau-leaping method to estimate second-order sensitivities of stiff stochastic biochemical systems, achieving comparable accuracy with reduced computational cost.

When do hybrid models help? Combining mechanistic modelling and machine learning to predict gene regulatory network dynamics

This study investigates when combining mechanistic and machine learning models improves predictions of gene regulatory network dynamics using synthetic data.

Simulation-free approximate Bayesian computation for stochastic reaction networks

This paper introduces SFABC, a simulation-free method for Bayesian inference of stochastic reaction networks, showing it matches simulation-based accuracy at lower computational cost.

Model-guided design of single-cargo, dual-attractor gene circuits

This paper designs single-cargo synthetic gene circuits that generate two balanced cellular states without relying on copy-number variation.

Using Bayesian inference to characterize cell-size regulation across organisms

This paper develops a Bayesian framework to accurately infer cell-size regulation strategies by accounting for noise and variability, showing that previous methods were biased and that adder-like regulation is more consistent across organisms.

Genetic Perturbation Prediction Models Fail Out-of-Distribution: A Rigorous Benchmark and Systematic Evaluation

This paper evaluates perturbation prediction models under out-of-distribution conditions and finds they perform poorly, highlighting the need for better generalization.

Polarization of regulatory inputs reveals structural constraints in reconstructed pathway-associated regulatory networks

The paper introduces a framework to analyze regulatory network polarization and finds that mixed-input targets are systematically depleted in pathway-associated networks.

Questioning the G2 phase in the budding yeast cell cycle with a qualitative and possibilistic model

The paper develops a possibilistic model to question the existence of a distinct G2 phase in budding yeast, supporting prometaphase instead and providing a framework to resolve cell cycle ambiguities.

Mapping Synthetic Circuit Design Space through Massively Parallel Measurement of Single-Cell Dynamics

The paper uses a high-throughput platform to measure single-cell dynamics of gene-regulatory circuits and maps their design space by testing robustness and identifying key interactions.

How Bacterial Growth-Phase Reshapes Synthetic Gene Circuit Function

This study develops a mathematical model to predict synthetic gene circuit function during bacterial stationary phase by measuring transcription and translation dynamics over time.

Certified Design Regions under Parametric Plant Uncertainty with Application to Antithetic Integral Feedback

The paper develops a method to compute certified design regions for robust controller tuning under parametric plant uncertainty, demonstrating improved certification rates using tensor-product Bernstein enclosures.

Empirical Bayes Estimation of Sample-Specific Correlation Networks for Cross-Sectional and Longitudinal Omics Data

PRISM estimates sample-specific correlation networks for cross-sectional and longitudinal omics data, improving accuracy and uncertainty quantification over existing methods.

Sense-and-response to intracellular signaling via nucleocytoplasmic localization control of a synthetic transcription factor platform

The paper develops a synthetic biology platform using nucleocytoplasmic localization tags to enable cells to respond to intracellular signals by inducing transgene expression.

Mathematical Modelling of Stochastic Gene Expression Quantifies mRNA Heterogeneity in Cancer Cell Populations with Extrachromosomal DNA

This study uses a stochastic gene expression model to quantify mRNA heterogeneity in cancer cells with extrachromosomal DNA and shows similar transcriptional parameters across different cell lines and amplification types.

Synthetic genetic circuits that count developmental cues in plant roots

This paper develops synthetic genetic circuits in Arabidopsis thaliana to count root development cues and enable differential gene expression in successive root branches.

Additive-input encoding fails operator-level target-held-out prediction on current Perturb-seq screens

The paper shows that additive-input models fail to generalize in Perturb-seq screens when predicting new perturbation targets.

Unified Network Spectroscopy: Inferring Reduced Biological Dynamics from Fluctuations under Structured Noise and Broken Ergodicity

This paper introduces Unified Network Spectroscopy (UNS), a method to infer biological dynamics from fluctuations, addressing issues with noise structure and ergodicity.

CardamomOT: a mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling

CardamomOT improves gene regulatory network inference and trajectory reconstruction by integrating mechanistic models with optimal transport, using exact time labels and reducing hyperparameters.

RI-SINDy: Regulation-Informed Sparse Model Discovery in Biological Gene Networks

RI-SINDy improves sparse regression for gene network modeling by addressing multicollinearity in regulatory nonlinearities, achieving better coefficient recovery and trajectory accuracy in both experimental and synthetic data.

From Network Topology to Parameterization: An Optimized Model of Endothelial Mechanobiology

The paper develops an optimized model of endothelial signaling using ODEs, showing that most responses can be captured with shared parameters, with only a few reactions needing specific parameterization.

Ultrasensitive response in bacterial replication initiation

This study identifies the mechanisms of ultrasensitivity in bacterial replication initiation, showing it arises from DnaA dynamics under different growth conditions.

Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT

The paper introduces scBOLT, a framework for inferring Boolean networks from multi-condition single-cell transcriptomes, improving computational tractability for large-scale models.