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Scientific Educational Development Lead, RCSB Protein Data Bank / Research Associate Professor, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, NJ, shuchi.dutta@rcsb.org
Shuchismita (Shuchi) Dutta is the Scientific Educational Development Lead at the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB). She is a trained crystallographer and is committed to promoting a molecular view of biology to a broad range of audiences. Dr. Dutta teaches an undergraduate honors seminar at Rutgers University where students learn the molecular structural bases of global health topics. She also teaches molecular visualization in graduate level courses at Rutgers and Georgetown University. She has collaborated with New Jersey high school educators to develop curricular materials for introducing students to biomolecular structure and functions. These materials are available from RCSB PDB’s educational portal, PDB-101, and used by students and educators worldwide. Dr. Dutta continues to work in a number of national outreach and educational efforts to promote molecular structural literacy – e.g. Protein Modeling at the Science Olympiad, RCSB PDB video challenge, and development of the PDB-101 Global Health pages. Recently, she has assembled a group of undergraduate educators from around the nation to form Molecular CaseNet, who are collaborating to develop and test molecular case studies.
Dr. Dutta earned her Bachelor of Science in Human Biology with a specialization in Biophysics, and a Masters in Biotechnology, both from All India Institute of Medical Sciences, New Delhi, India. She then came to the U.S. for graduate studies and completed her Ph.D. in Biophysics at Boston University School of Medicine and did a postdoctoral fellowship at Harvard Medical School, in Boston MA. Shuchi joined the Protein Data Bank at Rutgers University as a biocurator and has transitioned to become an educator. Her research is currently focused on understanding user community needs and developing products to promote a molecular structural view of biology and medicine.
Session: Pre-Conference Workshop, Hands-On Session
Session: Pre-Conference Workshop, Hands-On Session
DAY 1: September 11, 2019 | Hands-On Session | 4:15 PM - 5:30 PM
DAY 1: September 11, 2019 | Hands-On Session | 4:15 PM - 5:30 PM
Molecular Medicine with Three Dimensional Insights
Molecular Medicine with Three Dimensional Insights
Shuchismita Dutta, PhD, Rutgers University, NJ, USA
Today vast amounts and a variety of data enables the practice of precision medicine. In this approach, physicians, biomedical scientists, and data experts use computational tools to integrate information from genomics, proteomics, metabolomics, transciptomics, and digital health data for personalized assessments of disease risks, and to develop suitable management options. Visualization of the three-dimensional (3D) shapes and interactions of key molecules involved in disease processes can help integrate information from multiple disciplines, facilitate communication between the multidisciplinary experts, and provide new insights about the disease. The Protein Data Bank (PDB) provides access to experimentally determined 3D structures of over 155 thousand biological macromolecules and their various complexes. Visualization and analysis of relevant molecular structures can guide the design of new and/or precise treatments. Additionally, visualization of these molecular structures can help explain therapeutic rationales to patients and their families, which in turn can increase treatment adherence and persistence. Participants will be introduced to tools and resources that are freely available to visualize, explore, and analyze molecular structures. In the hands-on session they will explore selected examples of molecules that were critical to understanding and customizing treatment options for various disease states – such as endocrine disorders and cancer. Molecular visualization and explorations will enable participants to begin the process of integrating multidisciplinary data for developing new knowledge and for understanding personalized treatment options.
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