Gene Regulatory Network Analysis

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.

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.

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.

Spatially Resolved Reaction-Diffusion Modeling Reveals Effects of Intracellular Spatial Heterogeneity on Yeast Galactose Network Dynamics

The paper develops spatially-resolved models of the yeast galactose switch to show how intracellular organization affects regulatory dynamics.

PEtab Select: specification standard and supporting software for automated model selection

PEtab Select provides a standardized format and software for automated model selection in systems biology, supporting diverse modeling frameworks and enabling efficient evaluation of large model spaces.

Learning activator-inhibitor dynamics at the cell cortex with neural likelihood ratio estimation

The paper uses neural likelihood ratio estimation to infer activator-inhibitor dynamics in Rho GTPase activity from perturbation experiments in frog eggs.

GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference

GLM-Prior is a genomic language model that generates sequence-derived priors for gene regulatory network inference, showing strong performance in mammals but limited success in yeast.

Quantitative Model of Transcriptional Noise Regulation by mRNA Condensates

This study develops a theoretical framework to quantify how reversible mRNA condensation affects transcriptional noise and robustness.

Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State

This paper introduces a Bayesian method for inferring gene regulatory networks using a Chemical Langevin Equation model, enabling structure identification and kinetic parameter estimation without observing transient dynamics.

Cell state plasticity emerging from co-regulated, competitive, and configurable interactions within the AP-1 network

The paper develops an ODE model of the AP-1 network to explain population-specific state distributions and validates its predictions through experiments on melanoma cells.

Ratiometric transcriptional activation by protein degradation

This paper introduces ratiometric transcriptional activation to detect protein abundance changes by converting protein degradation into gene expression outputs in live cells.

Protocol for measuring endocrine disruptive effects on transcriptional bursting using single-molecule imaging in human breast cancer cells

This paper presents a protocol using single-molecule imaging to measure how endocrine disruptors affect transcriptional bursting in human breast cancer cells.

DDTRN: Predicting Bacterial Transcriptional Regulatory Networks Based on Gene Sequences using Dual Descriptor

DDTRN predicts bacterial transcriptional regulatory networks from gene sequences using a dual descriptor model, achieving high AUROC and AUPR scores across multiple bacterial species.

Synthetic Data to Explore Transcriptional Regulation of Differentially Expressed Genes in Ovarian Cancer

The paper uses synthetic data to model transcriptional regulation of differentially expressed genes in ovarian cancer, identifying genes with altered regulatory dynamics.

Using the simple telegraph model to decipher transcriptional burst regulation across genome-wide data

The paper uses a simplified telegraph model to analyze transcriptional burst regulation in scRNA-seq and smFISH data, revealing key regulatory patterns in mouse and human tissues.

Bayesian model discovery for reverse-engineering biochemical networks from data

This paper introduces a Bayesian sparse likelihood-free inference method to reverse-engineer biochemical networks from single-cell data, showing high accuracy and outperforming correlation-based methods.

Mixed positive and negative feedback loops drive diverse single-cell gene expression dynamics

The paper uses stochastic modeling and experiments to show mixed feedback loops in bacteria generate diverse gene expression dynamics.

Nanoscale protein clustering modulates the input-output response of cellular signalling pathways

The paper develops an agent-based simulator to study how nanoscale protein clustering affects cellular signaling responses, showing that clustering can digitize signals and alter cell behavior.

Boosting reliability when inferring interactions from time series data in gene regulatory networks.

The paper improves gene regulatory network inference from time series data by enhancing reliability through modified algorithms and priors, showing improved performance metrics.

Benchmarking biochemical networks generated by large language models

This study benchmarks large language models' ability to generate biochemical networks, finding moderate accuracy in signaling and metabolic networks but variable prediction performance.

Engineering compact bacterial combinatorial promoters for two-input chemical AND switching

The paper designs and evaluates compact bacterial promoters that respond to two chemical inputs with AND logic, finding that utility depends on minimizing leakage and matching operator-scaffold compatibility.

Biologically informed neural network models are robust to spurious interactions via self-pruning

This paper introduces a method to evaluate the robustness of biology-informed neural networks by measuring their ability to prune spurious interactions during training.

Inferring and simulating a gene regulatory network for the sympathoadrenal differentiation from single-cell transcriptomics in human.

The paper infers and simulates a gene regulatory network for sympathoadrenal differentiation using single-cell RNA-seq data from human cells, identifying a 97-gene network that drives the transition from Schwann cell precursors to chromaffin cells and sympathoblasts.

Design and optimization of a kinase-controlled allosteric switch

The paper designs and optimizes a phosphorylation-controlled transcription factor switch that outperforms existing ERK-responsive biosensors in selectivity and sensitivity.

SignifiKANTE: Efficient P-value computation for gene regulatory networks

SignifiKANTE computes efficient P-values for gene regulatory networks by clustering target genes to reduce computational cost.

Engineering microbial consortia for distributed signal processing

This paper introduces a method using microbial consortia and computational models to decode complex signals and predict input concentrations from microbial community responses.

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.

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.

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.