The Theodoris Lab studies how genes interact within networks to enable proper development and function of the heart, and how disruptions in those gene networks can result in cardiovascular disease. They use a combination of experimental biology and computational modeling to map the gene networks that drive disease and to design therapies that can correct the networks back to a healthy state.
Disease Areas
- Congenital Heart Disease
- Cardiovascular Disease
Areas of Expertise
- Machine Learning
- Cardiovascular Genetics
- Computational Biology
- Deep Learning
- Experimental Genomics
Lab Focus
- Mapping gene networks dysregulated in cardiovascular disease.
- Developing machine learning approaches to leverage large-scale biological data that enable predictions in network biology.
- Designing network-correcting therapies for cardiovascular disease.
Research Impact
Christina Theodoris is interested in determining the circuitry of gene regulatory networks disrupted in cardiovascular disease. With a strong foundation in pediatric cardiovascular genetics, her group moves from bedside to bench by using experimental models to understand the transcriptional and epigenetic mechanisms underlying disease. Leveraging cutting-edge experimental genomics and computational modeling, they map the gene networks that drive disease progression to identify central regulatory nodes that, when targeted, have a broad restorative effect on the network. They then use machine learning approaches to identify novel network-correcting therapeutics that target these central nodes to treat the core disease mechanism rather than merely manage symptoms. This approach has already led to the identification of a candidate therapeutic for cardiac valve disease that is under further development toward clinical trials, bringing discoveries back from the bench to the bedside.
The Theodoris Lab is also focused on developing novel machine learning methodologies to fully take advantage of large-scale biological data and new biological measurements made possible by recent advances in biotechnology. They design innovative modeling approaches to enable predictions in network biology, especially in settings where discoveries are impeded by limited data, including rare diseases and diseases affecting clinically inaccessible tissues.
