Holly A. Huber

Multiscale Probabilistic Modeling: A Bayesian Approach to Augment Mechanistic Models of Cell Signaling with Machine-Learning Predictions of Binding Affinity

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

Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation

This paper introduces a KL divergence-based Bayesian framework to systematically rank parameters for hypothesis formation, demonstrating its effectiveness in two computational models.

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells

The paper uses a computational model to show that phosphatase concentrations and prolactin receptor levels influence JAK-STAT signaling in pancreatic beta cells, suggesting targets for diabetes treatment.