Research Areas

Systems biology, an interdisciplinary and data-driven approach to biomedicine, is increasingly transforming biomedicine from disease-driven and reactive to health-driven and predictive. ECE researchers are applying computational bioinformatics, bioimaging, and mathematical modeling to understand the molecular regulatory and signaling pathway networks associated with biological processes.

Current Research

Drug resistance in breast cancer

ECE researchers are working with biologists to build mathematical models to predict the impact of drugs on cancer cells.

When using drug therapy to deprive breast cancer cells of estrogen, some of the cells die, but others eventually become independent of estrogen (drug resistant) and begin to grow again. We built a model that shows how cells transition from estrogen sensitivity to estrogen independence. The model was used to design an optimal cycle of drug therapy that minimizes the growth of the cancer while preventing drug resistance.

Genetic risk factors

More than 80 percent of the genetic risk factors that have been identified for heart disease, diabetes, and cancer have no obvious functional explanation for their role in the disease. We are using machine learning methods to infer the functional effects of the newly discovered genetic factors by integrating evidence from multiple-omics data, including chromatin accessibility, histone marks, DNA methylation, gene expression, and DNA conservation mapping. We developed a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches in DNA copy number analyses.

Muscular Dystropies

Using a large tissue bank of frozen muscle biopsies from patients, a team is applying computational bioinformatics to understand the genetic causes of different types of muscular dystrophy. The work involves identifying the causative genes, defining the range of gene mutations, and analyzing the consequences of the gene/protein defects.

Signal processing & cancer

An ECE team has developed several innovative signal-processing methods to analyze genomic data for cancer research. We applied pattern recognition, signal processing, and machine learning to multiplatform genomic data, unraveling complex molecular networks associated with the development and progression of cancers. Our goal is to discover new knowledge of estrogen receptor signaling, and ultimately use this information to identify new therapeutic targets for drug discovery.