Prof. Dr. André Martins
Adv. Precl. Metabolic Imaging and Cell Engineering
Email: Andre.Martins( at )med.uni-tuebingen.de
Phone: +49 7071 29 87487
Fax: +49 7071 29 4451
Prof. Andre F Martins, Ph.D.
studied Biochemistry at the University of Coimbra, Portugal. In 2013, he earned a joint Ph. D degree in Chemistry and Biochemistry from U. Coimbra, Portugal, and the CNRS / U. Orleans, France. In November 2013, he joined the group of Prof. A. D. Sherry at UT Dallas and UT Southwestern Medical Center, US, as a Research Associate scientist. He deepened his interest in MRI responsive sensors and molecular imaging approaches during this time. In 2019, he started a new position as a Research Group Leader at the Eberhard Karls University Hospital Tübingen (UKT) to lead the Hyperpolarized & Multimodal Contrast Imaging Science group. He is one of the 2020 recipients of the prestigious Alexander von Humboldt Foundation's Sofja Kovalevskaja Award. Since 2022 he has been appointed as a new professor at the faculty of medicine. The group changed its name to Advanced Preclinical Metabolic Imaging and Cell Engineering with this appointment.
Our team is interested in understanding relevant paradigms in human pathology and physiology through accurate non-invasive biomedical imaging. The team uses highly translational molecular and metabolic imaging approaches to determine the role of metabolism in different diseases in vivo. Our research is multidisciplinary, and placed at the intersection of several scientific fields in oncology, biomedical imaging, and fundamental sciences (biophysics, biochemistry, chemistry). The team is also interested in developing the next generation of non-invasive hybrid metabolic sensors for biomedical imaging.
Current Research Topics at the Advanced Preclinical Metabolic Imaging and Cell Engineering group:
-Multimodal/hybrid imaging of the tumor microenvironment & immunotherapy
-Tumor metabolic profiling, heterogeneity & aggressiveness
-Hyperpolarized metabolism, metal metabolism, and heteronuclear MRI
-Functional quantitative imaging with engineered hybrid sensors