Faculty and Staff

Lubna Pinky, Ph.D. joined Meharry SACS in January 2023 as assistant professor of biomedical physics. She is interested in biological systems and understanding them through a quantitative perspective using mathematical modeling.

Broadly, Dr. Pinky focuses on the application of mathematics to better understand infectious disease kinetics in the respiratory tract. In her work, she develops and analyzes clinically motivated and experimentally calibrated mathematical models of the immune response and treatment for pathogenic infections based on differential equations. She also uses optimization methods to investigate experimental data, suggest further experimental designs to validate predictions of the models, and generate novel hypotheses about the infection biology. Dr. Pinky is also interested in cancer biology.

Dr. Pinky was previously a senior research scientist at Eastern Virginia Medical School. There, she and a multi-disciplinary team of researchers and clinicians used proteomics data to discover novel and robust non-invasive biomarker signatures to distinguish prostate cancer patient risk groups. They also applied mechanistic modeling and machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis, and to build up an application prototype based on the models.

Dr. Pinky earned a Ph.D. in Biophysics from Texas Christian University in 2018, and completed postdoctoral training from the University of Tennessee Health Science Center in 2021.

Research Interests

• Quantitative Virology and Immunology: Modeling and analysis of viral load and immunological data to improve our understanding of the functioning of the immune responses against single and coinfection with viruses such as influenza virus, respiratory syncytial virus, parainfluenza virus and SARS-CoV-2.
• Mathematical Epidemiology: Application of SIR Models to predict epidemiology of more than one circulating pathogen in the population.
• Prostate Cancer Biology: Development of data-driven computational methods to predict presence or absence of prostate cancer (diagnosis), and low-risk or high-risk prostate cancer (prognosis) based on molecular, clinical, and demographic data; specifically, on measurements available in clinical settings which can be attained without performing biopsies on patients.