My research focuses on developing deep learning and mathematical frameworks that help researchers study cancer and prion diseases at finer scales, enabling them to perform more robust and accurate downstream analyses. Both cancer and prion diseases are epigenetic processes – where the disease is transmitted vertically during cell division, and the disease agent is encoded by the host cell itself.
My research tries to better understand the nature of epigenetic diseases by employing multi-scale complex computational models. So far, we have developed a 3D framework for modeling protein aggregation in dividing (deforming) yeast cells at a single-cell scale. This research demonstrated the asymmetric protein segregation known to be important during disease transmission and found critical diffusion and reaction regimes that affect the disease state. We have also developed the state-of-the-art deep learning model for generating realistic single-cell RNA sequencing data, called ACTIVA, which facilitates the analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.
My research's future direction is to understand the genetic basis of fatal diseases, such as cancer, Alzheimer's, and Creutzfeldt-Jakob, using a combination of mathematical modeling (PDEs, ODEs) and deep learning techniques.