Gene Regulatory Network Analysis

This paper develops an effective Markovian dynamics method to solve non-Markovian gene expression models and shows it explains reduced mRNA-protein correlation and enhanced bimodality in non-exponential protein decay.

This study introduces Growth Mechanics to show that metabolic oscillations in growing cells are necessary for maximizing fitness by optimizing resource allocation.

The paper models how transcriptional memory fluctuations and mitotic resetting influence cell cycle duration heterogeneities across lineage pairs.

The paper develops RNA-protein hybrid I1-FFL circuits in E. coli to enable tunable pulse dynamics through multi-level regulation.

This paper introduces a genetic algorithm-based workflow for refining biological network models using perturbation-observation pairs, demonstrating its effectiveness in a plant signaling model.

This paper introduces a distributed large-scale neural network model for yeast transcriptional regulation, capable of predicting dynamic regulatory relationships and identifying key transcription factors.

This study investigates how different DNA damage stimuli regulate p53 target gene promoter activity through stochastic gene expression, revealing stimulus-specific activation and deactivation rates.

The paper designs a multicellular PID controller using root contours for tuning control gains and validates it through in silico simulations.

The paper shows that noise in gene expression can differ between single-cell and population perspectives, with population-level noise being higher in some cases due to cell cycle dynamics and feedback loops.

This paper designs a thermal bioswitch to control macrophage activation in vivo, enabling spatially localized gene expression for at least 14 days after ultrasound heating.

This paper introduces RE array plasmids and the CTRL method to control gene expression by sequestering transcription factors in mammalian cells.

The paper introduces MERLIN+P+TFA to robustly estimate transcription factor activity and infer gene regulatory networks from bulk and single-cell data, showing improved accuracy in yeast and mammalian systems.

The paper introduces a framework called signaling homeorhesis to explain how cells interpret growth factor signals over time to produce distinct phenotypes.

The paper demonstrates that the Hill-type equation can describe both mRNA and protein expression steady states under p53 pulsing, showing they have similar fold changes and enabling proteome prediction from transcriptome data.

PRESAGE predicts perturbation expression responses using gene embeddings and outperforms existing methods in functional evaluations.

This paper develops a reaction-diffusion model to explain the spatial control of gene silencing during X chromosome inactivation, showing how feedback loops and chromatin modifications regulate the spread of silencing.

The paper introduces a domain decomposition approach to efficiently compute Markov State Models for stochastic gene regulatory networks with uncertainty quantification.

The paper designs and characterizes ComMAND, a microRNA-based gene circuit that precisely controls transgene expression in diverse primary cells.

The paper measures mRNA and protein levels in single cells for synthetic promoters, finding that promoter strength affects both transcription and translation, and introduces a framework for comparing gene regulation.

The paper introduces a model to identify decision-making classes in cell fate transitions using single-cell RNA-seq data, showing examples in lung and hematopoietic differentiation.

The paper develops a stochastic model of transcriptional responses to DNA supercoiling and demonstrates its control implications using an E. coli temperature-sensitive mutant.

The paper shows that genetic perturbations affect genes maintaining stoichiometry with the perturbed gene, revealing a multi-level stoichiometry-conserving architecture across species.

PerTurboAgent predicts gene panels for sequential Perturb-Seq experiments using an LLM-based agent, outperforming existing strategies in identifying phenotypically impactful genes.

This study develops a model to analyze how cellular memory and adaptation cost affect survival in fluctuating environments, finding that increased adaptation cost can improve growth by reducing mismatches between cell state and environment.

Gene2role embeds genes using multi-hop topological information from signed GRNs to identify topological changes across cell types and quantify module stability.

The paper introduces contextualized network inference to estimate sample-specific transcriptional networks for 7000 tumors, improving accuracy and enabling novel prognostic subtyping.

This paper introduces a multiscale probabilistic framework to improve cell signaling model parameter estimation by integrating protein sequence and structure data from databases.

This paper introduces a Bayesian Independent Component Analysis method to identify co-regulated gene modules and quantify uncertainty, improving integration of multi-strain and multi-omics data.

This paper designs genetic networks for stochastic and probabilistic computing in living cells, introducing concepts like bit-streams and p-bits to better model biological information processing.

TRENDY improves gene regulatory network inference by integrating transformer models into the WENDY method, showing better performance on simulated and experimental data.