Gene Regulatory Network Analysis

The paper introduces CARDAMOM, a method for inferring Gene Regulatory Networks from timestamped scRNA-seq data by leveraging metastability and transcriptional bursting.

The paper introduces a hybrid machine learning approach for perturbation biology to identify combinatorial therapeutic candidates using a melanoma cell line dataset.

The paper introduces a method to predict single-cell metabolite distributions using Gaussian mixture models, bypassing stochastic simulations by leveraging enzyme kinetics and expression data.

The paper develops a stochastic model of transcriptional processes to derive analytical expressions for nascent and mature RNA fluctuations and their dependence on transcriptional parameters.

The paper shows that stimulation rate, along with concentration, determines MAPK signaling and cell survival in budding yeast through a rate threshold mechanism.

The paper develops a mechanistic model of the BLADE platform to predict performance of 256 synthetic DNA recombination circuits, showing good agreement with experimental data and identifying factors affecting circuit performance.

The paper describes a broad host range inverter package for genetic circuit design in Gram-negative bacteria, showing its functionality in Pseudomonas putida and portability across species.

This paper designs two post-translational protein oscillators inspired by the KaiABC system, using reversible binding and phosphorylation/dephosphorylation reactions.

The paper models noise propagation in metabolic pathways with growth-mediated feedback, showing that enzyme fluctuations affect growth rate through the cascade.

The paper uses machine learning to identify gene modules in Bacillus subtilis, revealing known and novel regulatory roles and population-level sporulation states.

This study uses computational models to show that ratio-sensing in yeast galactose utilization arises from competition between carbon sources for transporters and transcription factors, and reveals design principles for synthetic biology applications.

The paper shows that subpopulations of sensorless bacteria influence fitness in fluctuating environments by affecting growth lags through gene expression noise.

This paper analyzes identifiability in stochastic differential equation models using case studies and synthetic data, showing they can extract more parameter information than deterministic models.

This paper introduces a Monte Carlo method called Contour Monte Carlo to estimate cell population heterogeneity from snapshot data without requiring predefined clusters.

The paper develops a biophysical modeling approach to simulate transcription and translation in cell-free synthetic circuits, showing it captures expression dynamics for two tested circuits.

The paper characterizes a synthetic bacterial oscillator using CRISPRi and shows it produces robust oscillations and affects cellular growth.

The paper investigates how competition for protein resources affects the dynamics of gene regulatory motifs using a kinetic model incorporating copy number variation and decoy binding.

The paper analyzes single-cell responses to growth factors using phosphorylation data to show cell-to-cell variation in signal transduction and suggests a bet-hedging strategy in fate decisions.

The paper develops a deep learning algorithm that integrates ordinary differential equations into neural networks to infer hidden dynamics and parameters from sparse experimental data.

The paper develops a spatio-temporal logical model of the Mitotic Exit Network (MEN) in budding yeast, revealing insights into protein localization and activity, and validating predictions experimentally.

This paper introduces GWNet, a graph-wavelet method for gene-network inference that improves consistency in single-cell transcriptome analysis, revealing age-related regulatory changes in lung cells.

This paper develops an endoribonuclease-based feedforward loop to improve modularity in genetic systems by reducing resource competition in mammalian cells.

PALLAS infers gene regulatory networks from time series data using penalized maximum likelihood and particle swarms, showing efficiency and accuracy in synthetic and real data.

FINET is a software tool that efficiently and accurately infers gene regulatory networks and other types of networks using parallel computations in Julia.

This study shows that decision-making in synthetic cells can shift from slow and precise to rapid and probabilistic by reducing the number of computing molecules to single-molecule levels.

This study uses deep generative models to learn representations of transcriptomic data from cells under various perturbations, revealing relationships between perturbagens and drug-target mappings.

scTenifoldNet constructs and compares single-cell gene regulatory networks using scRNA-seq data to identify gene expression changes in response to various biological conditions.

The paper evaluates methods to infer transcription factor (TF) activity from gene expression data using constraints from TF perturbation data, showing constraints are essential for performance.

The paper develops a theoretical framework to explain exponential growth in nonlinear reaction networks, showing how scalability of flux functions and ergodicity of rescaled systems lead to various growth modalities.

The paper introduces DReSS, a method to quantify reachability differences between state spaces of Boolean networks caused by structural perturbations, and demonstrates its application in detecting sensitive interactions in yeast cell cycle regulatory networks.