Rachel A. Gottschalk

The paper develops a rule-based model of TLR4 signaling in mouse macrophages, identifies a missing negative feedback checkpoint involving TANK and IKKε, and validates its role in restraining inflammation.

MitoChontrol improves single-cell RNA sequencing quality control by using a probabilistic framework to filter out compromised cells while preserving biologically relevant mitochondrial content.

The paper investigates gene regulatory networks in alveolar macrophages, finding that tissue-resident macrophages have more restrained inflammatory responses due to a stabilizing network involving PU.1 and CEBP/β.

The paper identifies context-dependent regulatory variants in Alzheimer's disease by analyzing their effects across different cellular contexts using MPRAs and machine learning.

The paper shows macrophages use TLR4 memory and IL-10 susceptibility gating to adjust inflammation based on pathogen dynamics.

The paper predicts gene sensitivity to JAK-STAT perturbation by linking STAT phosphorylation dynamics to gene expression using a mechanistic-to-machine learning framework.

The paper uses base editor screens to assess phosphorylation site functionality in T cell activation, identifying novel sites that modulate transcriptional activity and signaling pathways.