We use tools from computational and mathematical modeling to solve a variety of problems in toxicology.

A major focus of our work is the application of computational methods to study the signaling and transcriptional regulatory networks that underlie the determination of cell fate, and the perturbation of these networks by environmental pollutants like dioxin. We are integrating diverse genomic data sets to map and model transcriptional regulatory networks and their environmental perturbation. We are also interested in spatial multi-scale modeling of tissue-level phenomena like toxin-induced liver injury. We rely primarily on mathematical and statistical modeling as research tools, and work in close collaboration with experimental scientists.

Prediction of DNA – transcription factor binding

Predicting genome-wide transcription factor binding sites on DNA is a challenging problem. We are using machine learning tools to address this problem for members of the bHLH-PAS family of transcription factors.

Recent Publications:

  1. Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box motifs, Marri et al 2023.
  2. Predictive Models of Genome-wide Aryl Hydrocarbon Receptor DNA Binding Reveal Tissue Specific Binding Determinants, Filipovic et al 2023.

Modeling perturbations in single-cell gene expression

The space of possible human cell types and chemical treatments / doses is exponentially large. We are developing deep learning-based models to extrapolate chemical dose-response across cell types, doses and chemicals.

Recent Publications:

  1. Generative Modeling of Single Cell Gene Expression for Dose-Dependent Chemical Perturbations, Kana et al, 2023.

Predictive modeling of cellular transitions in health and disease

We are integrating and analyzing bulk and single-cell gene expression data as part of a multi-lab project investigating the role of perivascular adipose tissue in cardiovascular disease.

Recent Publications:

  1. Phenotypic Changes in T Cell and Macrophage Subtypes in Perivascular Adipose Tissues Precede High-Fat Diet-Induced Hypertension, Kumar et al, 2021.
  2. Blood pressure changes PVAT function and transcriptome: use of the mid-thoracic aorta coarcted rat, Contreras et al, 2020.