Lindsay Cowell, Ph.D.

Download Curriculum Vitae

Dr. Cowell received a M.S. in Biomathematics with a minor in Mathematics in 1995 from North Carolina State University. In 2000, she received a Ph.D. in Biomathematics with a minor in Immunology, also from North Carolina State University. She spent three years as a postdoctoral fellow in the Department of Immunology at Duke University Medical Center and then became an Assistant Professor in the Department of Biostatistics and Bioinformatics.  She was also on the graduate faculty at Duke for the Computational Biology and Bioinformatics Graduate Program. In September 2010, she joined the Biomedical Informatics Division in the Department of Clinical Sciences at UT Southwestern. She has remained with that Department as it became the Department of Population and Data Sciences and is now the Peter O'Donnell Jr. School of Public Health. She has a secondary appointment in the Department of Immunology in the School of Biomedical Sciences and is affiliated with the Immunology, Cancer Biology, and Computational and Systems Biology graduate programs.

Adaptive Immunity

Dr. Cowell is broadly interested in understanding the mechanisms of adaptive immunity and their role in health and disease. The adaptive immune system is arguably the most variable system in the body in terms of person-to-person variability and changes within an individual over the course of life. At the level of DNA, the germline and somatic variation of the Major Histocompatibility Complex and Adaptive Immune Receptor loci are what make us most unique. As a consequence of this variation, the adaptive immune system has a remarkable ability to detect and respond to a vast and constantly shifting array of pathogens and other threats, such as cancer. The cell surface receptors, adaptive immune receptors, responsible for recognizing these threats, are encoded by genes that are somatically generated in developing lymphocytes. Every individual has billions of such genes, the full set of which (the repertoire), is unique to each individual and changes over time in response to the individual’s exposures.

Research in the Cowell group is directed toward advancing understanding of (1) the molecular mechanisms by which adaptive immune receptor genes are somatically generated and diversified, (2) the role of these mechanisms in health and disease, and (3) the dynamics of adaptive immune receptor repertoires over the course of immune responses and life. In addition to our basic science research, we have pursued clinical applications in the areas of autoimmune disease (e.g., multiple sclerosis), infectious disease (e.g., Staphylococcus aureus, HIV), and cancer immunology (e.g., HPV-related cancers, particularly cervical cancer, ovarian cancer, kidney cancer).

The tremendous size and diversity of adaptive immune receptor repertoires require the use of high-throughput technologies and big data analytics for their study. Thus, we leverage our expertise in bioinformatics, statistics, and machine learning to develop novel computational approaches to address the above research questions. Our current projects are focused on developing novel methods for:

• Representing repertoires not as sets of billions of nucleotide or amino acid sequences, but rather as landscapes of antigen specificities and affinities;
• Determining and measuring the nature and degree of changes in the landscape over time and in response to particular kinds of exposures (e.g., infection, vaccination, cancer immunotherapy); and
• Detecting patterns in repertoires indicative of past or current immunological events and that can be used for diagnosis, prognosis, and precision medicine.

Computable Representations of Descriptive Biological and Clinical Information 

Dr. Cowell’s research in this area has focused on using formal logics to represent and compute with biological and clinical information in the immunology and infectious diseases domains. She is interested in using logical representations to enhance the analysis of high-throughput biological data and its integration with electronic health record data.