Integrated network analysis of transcriptional response: Highly complex gene regulatory networks guide how the host responses to the intervention by the disease including cancer and infection by a pathogen. Identifying systemic factors contributing to host response against a disease and pathogen survival within the host is a multi-dimensional problem that requires a systems approach. At Baliga lab, Dr. Immanuel’s research focuses on developing new experimental and computation strategies to predict drugs and drug combinations for improved personalized therapies against infectious diseases and cancers.
Baliga lab implements network-driven approaches to predict complex interactions within biological gene regulatory networks. Dr. Immanuel’s research would employ a holistic approach that incorporates different datasets onto networks with the help of machine learning algorithms. Integrated context-specific genome-scale gene regulatory and metabolic networks are being developed to identify drug targets (transcriptional factors and gene pairs). Drug combinations with higher efficacy and specificity will be identified based on this approach and will be tested for their activity using experiments. Her broader research scope is to innovate a scalable framework for network-driven drug discovery and translation of foundational concepts from the lab to clinical applications.
Currently, she is working on establishing techniques for integrating genome-scale regulatory networks and metabolic models of host-pathogen interactions during Mycobacterium tuberculosis infection. She also focuses on incorporating the experimental data of transcriptional responses from the host and pathogen under various conditions onto these integrated network models to identify transcription factor influences and predict context-specific drug targets.