The Das group strives to predict how sequence codes for structure in proteins, nucleic acids, and heteropolymers whose folds have yet to be explored. We use new computational and experimental tools to tackle the de novo modeling of protein and RNA folds, the high-throughput structure mapping of riboswitches and random RNAs, and the design of self-knotting and self-crystallizing nucleic acids.
Research focus is on building computational models of complex biological processes and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory, and signaling networks as well as intercellular interactions. Current research involves the dynamic behavior of NF-kappa B, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.
Lab develops and maintains the Saccharomyces Genome Database (SGD). The SGD provides information and tools on budding yeast genome, its products and their interactions. Several computational tools have been developed to provide to allow the research community to explore the collected data sets. Tools for querying >50,000 full-text papers are also provided. SGD has become an essential research tool used daily by thousands of researchers around the globe. Dr. Cherry’s second area of research is in the creation of ontologies to aid communication between biologists as well as biological database projects. His group is a founding member of the Gene Ontology (GO) Collaboration.
In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community’s latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously learning health system. My group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine that will deliver better care than what either can do alone.