Stefano Tiziani, Ph.D.







Dr. Stefano Tiziani is an Associate Professor in the Department of Nutritional Sciences at UT at Austin; in addition, he currently holds a courtesy appointment at the UT Austin Dell Medical School (Department of Oncology and Department of Pediatrics) and an adjunct position at the Medical School of UT Health Science Center at San Antonio. His laboratory is located at the Dell Pediatric Research Institute.

After receiving hisĀ Laurea in Chemistry at the University of Trieste, (Italy), Dr. Tiziani completed his Ph.D. at The Ohio State University in 2006, where he developed a strong interest in phytochemicals and their role in diseases prevention. His interest in metabolic biomarker identification was subsequently extended during his postgraduate training in the field of translational chemical biology to study cancer metabolism. He completed postdoctoral training in molecular oncology and metabolomics as a Marie Curie Fellow and European Research Fellow at the University of Birmingham in the United Kingdom in 2009. He then served as a National Science Foundation Fellow at the Sanford-Burnham Medical Research Institute in La Jolla, CA where he gained a better understanding of the role of the tumor microenvironment in childhood leukemia. Since September 2012, he has been a faculty member of UT at Austin.

Research interests in his laboratory focus on translational chemical biology using a cutting edge metabolomics-based systems biology approach for metabolic biomarker discovery. His laboratory combines i) high-throughput screening measurements, ii) magnetic resonance spectroscopy and mass spectrometry-based metabolomics, iii) metabolic flux analysis and iv) other omic data to gain a better bio-mechanistic understanding of the effects of combined drug treatment and nutrient modulation in cancer and non-cancer conditions. His current research is actively devoted to developing, integrating and correlating high-throughput screening and untargeted metabolomics data to identify cancer vulnerabilities and accelerate the identification of novel synergistic combinatorial treatment for more precise controlled of complex biological systems.