Computational Toxicology

Computational Toxicology

Our computational biology team uses modeling and high-content data to deepen our understanding of how chemicals interact with biological systems. We combine expertise and tools from different disciplines to develop innovative strategies for using large-scale data to solve problems related to chemical and drug safety. We have extensive experience in the analysis of genomic and high-throughput toxicology data for addressing dose response, time course response, and determination of cellular modes of action.

We use a variety of statistical and visualization approaches to understanding changes at the genomic level, including novel approaches to interactively displaying ontology enrichment patterns from both in vitro and in vivo exposure studies. Together, these tools and expertise allow us to help our sponsors interpret data from modern, high-throughput experiments and understand the impact of their products on human health

Our Expertise

  • Interpretation of gene expression data (microarray, qPCR, RNA-seq)
  • Next-gen sequence analysis (ChIP-Seq, targeted sequencing)
  • Predictive modeling and biomarker discovery
  • Benchmark dose analysis
  • Tool development for high throughput toxicogenomic data analysis and interpretation
  • General statistics and quantitative modeling: R & BioConductor, JMP Genomics & SAS
  • Novel tool development in Python (e.g. GOFigure Maps software)
  • JAVA-based integrated programming environments (e.g. KNIME) for automation of analysis pipelines components
  • Benchmark dose analyses of genomic data: BMDS and BMDExpress