Gene Regulatory Network Analysis

This paper introduces a mathematical model to predict molecular behaviors in cell plasticity using omics time-series data and demonstrates its ability to make non-trivial predictions.

This paper develops a genetically encoded protein oscillator system for real-time single-cell data streaming, demonstrating its use in tracking developmental states during differentiation.

geneRNIB provides a benchmark for gene regulatory network inference, showing simpler models often outperform complex ones in perturbation-informed metrics.

The paper improves Modular Response Analysis by addressing noise sensitivity, enabling non-independent perturbations, and incorporating nonlinearity and prior knowledge.

The paper presents an algorithm to convert Petri net models of biological signaling networks into annotated influence graphs, demonstrating it on the beta-adrenergic receptor pathway.

This study identifies acetylation of the Integrator complex as a key determinant of gene-specific transcriptional bursting patterns through high-throughput imaging screens.

This paper shows cells use a spatial representation to perform probabilistic source seeking through Bayes filtering, adapting to noise without explicit statistics.

This study uses transfer entropy to analyze information flow in the RAS-MAPK system, revealing bidirectional feedback and identifying functional disorder in a Noonan syndrome-associated SOS mutation.

The paper demonstrates temporal dose inversion in adaptive biomolecular circuits using synthetic gene circuits and identifies key parameters for this behavior.

The paper corrects claims that their solution for a self-repressing gene is incomplete and incorrect, showing the decaying rates are correct and the operator is self-adjoint with real eigenvalues.

CellProphet infers dynamic gene regulatory networks using AI, outperforming existing methods in differentiation datasets and accurately predicting cell fate transitions in mouse models.

This paper designs a biomolecular controller to reduce mutation spread in engineered cell populations by linking mutant growth to circuit design parameters through resource-aware modeling.

This paper shows that concentration buffering and noise reduction in phase separating systems are distinct concepts, neither of which is required or sufficient for the other.

This study investigates the role of genetic frustration in cell reprogramming by constructing a Boolean genetic network model and shows its critical role in initiating cell fate transitions.

This paper introduces CircuiTree, a Monte Carlo Tree Search framework for designing biochemical circuits, finding robust designs for oscillators and identifying novel fault-tolerant strategies.

scGRIP improves gene regulatory network inference in single-cell multi-omics data by integrating prior knowledge and explainable AI techniques.

The paper introduces a method to infer interaction directionality in biochemical networks from stationary measurements and validates it through simulations.

This paper introduces a temperature-inducible protein module called Melt that allows reversible control of mammalian cell processes through temperature changes, demonstrating its utility in cell death control in a mouse cancer model.

The paper analyzes the robustness and sensitivity of a bistable biomolecular circuit under multi-parametric perturbations, revealing a tradeoff between robustness and dynamic sensitivity.

This paper introduces SwitchTFI, a method to identify transcription factors driving cell differentiation using single-cell RNA sequencing data, and shows it outperforms existing methods in functional coherence.

This paper introduces a fluorescein-based SynNotch system to regulate gene expression in response to extracellular cues, demonstrating its use in controlling myogenic conversion.

The paper measures association and dissociation rates of LacI binding sites in living cells and finds an anti-correlation between these rates, challenging the assumption that binding site strength is solely determined by dissociation rate.

The paper constructs context-specific brain gene regulatory networks from bulk and single-cell RNA-seq data using Bayesian and deep learning methods to study Autism Spectrum Disorder mechanisms.

This paper develops hDIRECT, a platform using human-derived proteins to control cytokine activity in cell-based therapies, demonstrating orthogonalization through protease regulation.

This study uses a minimal model and ACME method to show that Cro2 binding at OR3 and CI2 at OR1 affect lysogeny stability during prophage induction.

This study uses RE:IN to characterize GRN ensembles in a mouse kidney injury model, identifying four GRN families and two substructures that distinguish them.

The paper designs synthetic promoters that respond to TFs differentially expressed in activated and exhausted T cells, enabling functional state classification and dynamic gene expression control.

This paper introduces a differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits, demonstrating its effectiveness with E. coli promoter models.

This study fine-tunes a genetic toggle switch by varying host contexts and ribosome binding sites to optimize performance metrics like signaling strength and inducer sensitivity.

The paper investigates how diffusion and protein copy number affect information transmission in yeast signaling systems, finding that active diffusion and higher copy numbers improve accuracy but increase entropy production.