Gene Regulatory Network Analysis

This paper introduces SCCVAE, a hybrid model combining mechanistic causal models with deep learning to predict and simulate genetic perturbation responses in single-cell data.

The paper investigates the relationship between gene expression plasticity and regulatory network topology in Escherichia coli, finding that plastic genes are regulated by more genes but not by specific network motifs.

This paper introduces CytoTape, a tool for continuous, multiplexed recording of gene regulation dynamics over weeks at single-cell resolution, and demonstrates its use in measuring transcription factor activities.

The paper introduces the Coli Toolkit (CTK), a modular cloning system for E. coli, expanding on the Yeast Toolkit to enable efficient genetic circuit assembly.

This paper introduces a moments-based framework to infer transcriptional kinetics from nascent RNA data, showing improved accuracy in model selection and distinguishing promoter activation mechanisms.

This study identifies central transcription factors in lung adenocarcinoma progression by analyzing gene regulatory network transitions and reveals their role in intratumor heterogeneity.

The paper introduces CLASSIC, a platform that maps sequence-to-function relationships in synthetic biology by combining long- and short-read sequencing to analyze gene circuits.

This paper introduces a deep learning model to predict gene regulation relationships for both known and unknown transcription factors, achieving higher accuracy for unknown TFs than existing methods.

The paper introduces 1100 synthetic benchmark problems for dynamic cellular process modeling to improve algorithm evaluation.

This paper develops a risk-averse optimization framework combining Bayesian inference and Thompson sampling to design robust genetic circuits despite model inaccuracies and parameter uncertainty.

This paper introduces a statistical model to estimate transcription factor activity from gene expression data by leveraging gene regulatory networks, improving interpretation of biological differences.

This paper develops and characterizes an inducible biosensor for arsenic in E. coli using naringenin, showing its tunable response and limitations in dynamic range.

GeneSNAKE is a Python package that simulates gene regulatory networks and perturbation-induced expression data for benchmarking, offering control over network properties and noise models.

The paper develops mathematical models of synNotch-CAR T-cells and performs sensitivity analysis to identify parameters that can be engineered for dynamic tunability in cancer therapeutics.

MINI-EX version 2 infers cell-type-specific gene regulatory networks in plants using single-cell transcriptomics and TF motif integration, improving accuracy and enabling analysis of non-model species.

SVGRN infers cell-specific gene regulatory networks from spatial transcriptomics data, showing improved performance in noisy and complex datasets.

This paper introduces MaLoN, an unsupervised method for inferring gene regulatory networks from in vivo data, showing improved accuracy over existing methods in yeast and E. coli.

DANSE models time-series multi-omics data to identify key transcription factors driving biological processes, using iPSC differentiation datasets as examples.

This paper introduces a noise-robust control mechanism for biomolecular systems that maintains output stability and reduces noise at the single-cell level.

The paper examines how temperature changes affect a kinetic model of E. coli metabolism and identifies strategies to maintain ATP production efficiency.

The paper proposes novel oscillator-based circuit designs for synthetic biology and models interactions of intracellularly coupled oscillators, predicting dynamic behaviors and potential applications in bacterial systems.

This paper introduces an algorithm for hierarchical decomposition of biochemical networks using causality and information flow, demonstrating computational efficiency and scalability through benchmarking.

The paper introduces DIMMERs, miRNA-based circuits that enable precise, tunable control of transgene expression across cell types, improving gene therapy and research applications.

This paper introduces scCNR, a method to reconstruct signal transduction networks from single-cell protein data by treating stochastic variation as perturbations, and validates it on simulated and experimental data.

This paper introduces a reaction-diffusion model called LAGS to show that eukaryotic receptors can perform directional sensing through lateral diffusion, basal activity, and active degradation, without relying on downstream signaling pathways.

This paper introduces a method to detect causal gene interactions using natural expression fluctuations in genetically identical cells, validated in E. coli synthetic circuits.

The paper develops a dynamical system model to analyze how Notch signaling and epigenetic modifications influence melanoma cell fate transitions during metastasis.

The paper shows that combining three indirect repression mechanisms maintains ultrasensitivity and noise robustness in transcriptional switches under realistic biological conditions.

Airqtl accelerates single-cell eQTL mapping and enables cell state-specific causal gene regulatory network inference using efficient linear mixed models.

This study introduces a generative model to infer epigenetic landscapes from data, demonstrating its application in developmental biology examples.