Gene Regulatory Network Analysis

This study uses Boolean modeling to analyze Parkinson's disease mechanisms, identify drug targets, and simulate treatment responses using the PD-map framework.

The paper introduces a Python package for simulating and analyzing multivalent biomolecular clusters using stochastic simulations.

This paper introduces a dynamical low-rank method to solve the chemical master equation for biological reaction networks, enabling efficient simulation of large systems.

The paper proposes a computational framework to analyze single-cell experiments and optimize design to extract fluctuation information while reducing measurement noise.

The paper examines moment-estimation methods for chemical reaction systems with heavy-tailed distributions, showing their limitations and inconsistencies.

The paper shows that stochastic fluctuations in Prrx1 transcription factor activity contribute to cell-to-cell variability in Hedgehog signaling response in NIH3T3-CG cells.

The paper quantifies the information transmission rate of the MAPK/ERK pathway, finding it can transmit at least 6 bits per hour.

This paper presents an optimization-based method to design synthetic gene circuits with specified bifurcation diagrams, focusing on mushroom bifurcation for applications in biosensors and synthetic biology.

The paper develops a method to infer autoregulation in gene expression using variance-to-mean ratios and applies it to experimental data to identify potential autoregulated genes.

The paper constructs synthetic genotype networks of gene regulatory networks in E. coli to study robustness and evolvability in genotype-phenotype maps.

The paper analyzes information transfer in central dogma processes, showing that transcription and translation form a time-integrating channel that maximizes information gain while minimizing stochastic loss.

The paper analyzes eight integral controller motifs in vertebrate photoadaptation, finding two classes of controllers with distinct responses to background changes and their relation to psychophysical laws.

The paper develops a method to select Boolean gene regulatory network models by leveraging developmental landscapes and attractor stability, finding that a recent model fails to respect expected cell state hierarchies and proposing an algorithm to correct this.

The paper investigates how yeast promoters decode the light-controlled localization dynamics of the TF Msn2 and shows that promoter-dependent effects of changing Msn2 DNA binding affinity influence gene expression.

The paper investigates how mammalian promoters decode transcription factor dynamics using optogenetics, deep learning, and mathematical modeling, finding that inefficient coupling between TF binding and transcription initiation enhances decoding.

The paper analyzes regulatory nonlinearity in biological networks using Boolean models and finds that biological regulation is generally less nonlinear than expected, with cancer networks showing higher and more variable nonlinearity.

This paper develops an integrase toolbox to record gene-expression during plant development by using orthogonal serine integrases and fluorescent reporters.

This paper introduces a generalized telegraph model and Bayesian inference method to infer transcriptional burst kinetics from single-cell data, showing improved accuracy over classical models.

The paper investigates how transcription burst amplitude modulation mediates dosage compensation in Drosophila embryos by analyzing gene expression dynamics.

The paper integrates modeling and synthetic biology to show that transcriptional synergy depends on both kinetic regulation of the transcription cycle and TF binding.

The paper reformulates modular response analysis as a multilinear regression problem to improve stability and performance in inferring biological networks from perturbation data.

This paper introduces DELAY, a deep learning method for inferring gene-regulatory networks from single-cell data, showing improved performance over existing methods in identifying causal relationships.

The paper presents two methods for precision engineering of genetic sensors using large-scale data and machine learning to achieve quantitative dose-response specifications.

This paper combines two methods to use a single gene regulatory network model for both inference and simulation of transcriptional profiles from time-course single-cell data.

The paper shows that growth rate variations alter gene expression responses, leading to bifurcations in cell-fate decision landscapes.

This paper combines machine learning with mechanistic models to improve cell-line-specific drug response predictions using large datasets.

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.

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.