Gene Regulatory Network Analysis

This paper develops a control-theoretic framework for engineering closed-loop cell-based therapies using nullgram profiling and a digital twin platform to achieve robust regulation of cellular behavior.

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 quantifies how growth rate and protein production fluctuations contribute to gene expression noise in E. coli, finding growth rate fluctuations are largely independent and decrease with mean growth rate.

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.

The paper explains how stochastic biochemical reaction networks generate heritable short-term cellular memory through molecular concentration variance, with implications for cell fate and environmental responses.

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.

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.

CausalGRN infers causal gene regulatory networks from single-cell CRISPR screens and predicts responses to unseen perturbations with improved accuracy.

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.

This paper introduces a Bayesian hierarchical framework to quantify both inter- and intra-experimental heterogeneity in dynamical processes using differential equations, demonstrating its robustness and flexibility.

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.

The paper introduces scstruc, an R package for learning and evaluating Bayesian networks from single-cell transcriptomics data to infer causal gene regulatory relationships.

The paper introduces a framework for engineering tunable oscillatory protein expression using Tet-On and dTAG degron systems, demonstrating synchronous oscillations with the COD system.

The paper shows that cells can detect chemotactic gradients with a minimal differential receptor activation of 0.026%, challenging the traditional ΔC framework.

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 synthetic biology framework that enables tunable differentiation ratios in microbial consortia, validated in a mouse tumor model.

The paper introduces scPII, a metric to quantify gene perturbation impact on single-cell transcriptomic networks using gene regulatory networks, showing strong correlation with CRISPR screen results.

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.