Gene Regulatory Network Analysis

The paper uses a topology-based theory to predict and validate independent control of G2-M and G1-S checkpoints in fission yeast cell cycle by analyzing CDK-cyclin complex dynamics.

The paper develops a method to engineer synthetic cis-regulatory elements that control gene expression in response to signals, showing they can be designed using quantitative rules.

This study investigates toggle-n networks to understand the dynamics of differentiation into multiple cell states, showing co-expression of transcription factors and the role of cytokine signaling and regulatory asymmetry.

This study introduces AUC-PR as a better metric for evaluating in silico perturbation models' ability to predict differentially expressed genes, showing it outperforms R2 in capturing biologically relevant outcomes.

This paper develops a terminator strength prediction model and a GAN-based terminator sequence generator for E. coli, achieving high prediction accuracy and reliable sequence design.

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.

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

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 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 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.

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 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.

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.

This paper introduces an optimal algorithm for inferring asynchronous Boolean networks from experimental data, balancing model fit, size, and computational efficiency.

The paper introduces a framework for training metabolic kinetic models using neural ODEs, demonstrating efficient parameterization on SBML models and glycolysis data.

The paper benchmarks methods for inferring gene regulatory networks from multimodal single-cell data and introduces a framework called GRETA to evaluate their performance and limitations.

ScPectral infers dynamic transcription network motifs using hypergraphs to identify developmental pathways from single-cell data.

This paper develops non-equilibrium indicators to predict tipping points in cell fate decision models, showing improved predictive power over traditional methods.

This paper designs and implements synthetic gene circuits to program axial elongation in mammalian cell aggregates by controlling proliferation, tissue fluidity, and cell-cell signaling.

The paper develops a method to infer biomolecular models from qualitative behaviors using Signal Temporal Logic and genetic algorithms, with gradient-based parameter estimation.

Scouter predicts transcriptional responses to genetic perturbations using LLM embeddings, achieving higher accuracy than existing methods.

The paper models the tradeoffs of decision-making and feedback control in organisms under changing environments, showing when control mechanisms are most beneficial.