Michael Irvin

Folding-Driven Control of the Functional PP1 Complex via Multiscale Modeling

The paper develops a multiscale model to study how conformational dynamics regulate the PP1-GADD34-eIF2 complex, showing folding-dependent assembly enhances biochemical function.

Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

The paper introduces PyDyNo, a framework to identify dominant reaction paths in biochemical networks using Bayesian parameter optimization, revealing distinct signal execution modes in apoptosis and JAK2/STAT5 networks.

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

GenSLMs use large language models to analyze SARS-CoV-2 genomes, enabling rapid identification of variants of concern and providing insights into evolutionary dynamics.

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq

The paper introduces a negative binomial mixture model to denoise LIBRA-seq data, improving antigen binding predictions across multiple donors.

Predictive uncertainty in mechanistic models of cellular processes calibrated to experimental data

The paper introduces a Bayesian and machine learning approach to assess predictive uncertainty in mechanistic models of apoptosis using experimental data, showing that non-quantitative data can improve model accuracy when combined.