Advancements in high-throughput multi-omic technologies have drowned us in a sea of biological data. I aim to develop mathematical methods that leverage this data deluge towards a more predictive biology. My research is focused on the data types of genomics, transcriptomics, and metabolic networks.
- Erol S. Kavvas et al. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. In review, 2018
- Erol S. Kavvas, Yara Seif, James T. Yurkovich, Charles Norsigian, Saugat Poudel, William W. Greenwald, Sankha Ghatak, Bernhard O. Palsson, Jonathan M. Monk Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions. BMC Systems Biology 2018 12:25
- Nathan Mih, Elizabeth Brunk, Ke Chen, Edward Catoiu, Anand Sastry, Erol Kavvas, Jonathan M Monk, Zhen Zhang, Bernhard O Palsson ssbio: a Python framework for structural systems biology. Bioinformatics, 2018
- Bin Du, Daniel C. Zielinski, Erol S. Kavvas, Andreas Dräger, Justin Tan, Zhen Zhang, Kayla E. Ruggiero, Garri A. Arzumanyan and Bernhard O. Palsson Evaluation of rate law approximations in bottom-up kinetic models of metabolism. BMC Systems Biology, 2016 10:40