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 primarily focused on the data types of genomics, transcriptomics, and metabolic networks.
-  Erol Kavvas et al. Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions. In review, 2018
-  Erol Kavvas et al. Machine learning and structural analysis of 1,595 Mycobacterium tuberculosis strains identifies targets of antibiotic resistance selection. In preparation, 2018
-  Bin Du, Daniel C. Zielinski, Erol Kavvas, et al. Evaluation of rate law approximations in bottom-up kinetic models of metabolism. BMC Systems Biology, 2016