Brian E. Munsky

The paper introduces a model explaining the transcriptional gradient in NNSVs through polymerase parameters, enabling genotype-based prediction of transcriptional behavior.

The paper introduces SSIT, a toolkit for stochastic modeling and analysis, demonstrating its application on yeast and breast cancer datasets.

MicroLive is an open-source image processing tool for live-cell single-molecule microscopy that provides GUI-based quantification of key imaging tasks.

The paper uses positive-unlabeled machine learning to identify potential riboswitch elements in human mRNA 5'UTR sequences by comparing them to known riboswitch sequences.

This paper develops a sequential experiment design strategy to efficiently identify discrete stochastic models for gene expression using single-cell data.

The paper uses single-cell experiments and stochastic modeling to uncover mechanisms of GR dynamics and DUSP1 transcription regulation in response to glucocorticoids.

This paper shows that noise-exploiting controllers can effectively manage multi-cell systems with time delays and model uncertainties, enabling distinct control of multiple cells with a single input.

This paper proposes a hybrid computational pipeline combining mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments capable of resolving multiple mRNA species.

The paper proposes a computational framework to analyze single-cell experiments and optimize design to extract fluctuation information while reducing measurement noise.

This paper extends previous work on Optogenetic Maxwell Demons to analyze the robustness of noise-based control under system volume changes, time delays, and model uncertainties.

The paper demonstrates that a single optogenetic input can control identical cells to track asymmetric and asynchronous reference signals by cycling through different stochastic controllers.

This paper demonstrates how noise and feedback can be used to control multiple synthetic cells with a single optogenetic input through a stochastic SIMO controller.

The paper uses single-cell mRNA visualization and mathematical modeling to show how inhibitors block IL-1β and TNF-α expression and reveal their activation pathways.

The paper uses live-cell imaging to study RNA polymerase II phosphorylation dynamics at a single gene, revealing spatial and temporal organization of phosphorylation clusters during transcription.

The paper uses live single-cell imaging and computational modeling to show that diverse kinetic stimulations improve prediction of signaling dynamics by better constraining model parameters.

The paper introduces a parallel implementation of a modified Finite State Projection algorithm to solve the chemical master equation more efficiently.

This paper develops and validates an FSP-FIM method to optimize single-cell experiments for parameter estimation in yeast stress response models.

The paper develops a computational model to simulate single-RNA translation dynamics and evaluates experimental assays for estimating elongation kinetics.

The paper develops a machine learning tool to predict dissolved organic carbon from soil microbiome data and finds that it performs comparably to traditional methods.

The paper develops a label-free method using flow cytometry and machine learning to predict algal lipid content without fluorescent dyes.

This paper introduces two Bayesian inference methods for stochastic gene expression parameters using multifidelity models, showing significant computational speedup compared to full FSP approaches.

This paper introduces a method to optimize single-cell experiments by estimating information using a finite state projection based Fisher information matrix, without assuming distribution shapes or requiring precise higher-order moments.

The paper develops a computational approach to model gene regulation by addressing distributional challenges in single-cell mRNA data, leading to predictive insights into transcription mechanisms.

The paper illustrates how different computational approaches and models can yield varying results when analyzing single-cell gene regulation data, using simulated data and software tools.