Sadjad Fakouri Baygi, PhD, a postdoctoral fellow at the Icahn School of Medicine at Mount Sinai where he is a trainee at the Institute for Exposomic Research, received the Mark P. Styczynski Early Career Award in Computational Metabolomics from Metabolomics Association of North America (MANA) for his accomplishments in the computational metabolomics field
Sadjad Fakouri Baygi, PhD, a postdoctoral fellow at the Icahn School of Medicine at Mount Sinai where he is a trainee at the Institute for Exposomic Research, received the Mark P. Styczynski Early Career Award in Computational Metabolomics from Metabolomics Association of North America for his accomplishments in the computational metabolomics field: developing computational mass spectrometry software packages. The research was conducted under the mentorship of Dr. Dinesh Barupal. An interview with Dr. Fakouri Baygi:
Tell us about your research.
I am a data scientist with a focus on computational mass spectrometry for metabolomics and exposomics studies. I develop new algorithms, pipelines and software packages to interpret population-scale datasets to study the relationships among chemical exposures and metabolic pathways. My current research focuses on understanding exposome-induced metabolic disturbances in early life at prenatal and postnatal stages. My computational methods streamline the analysis of complex data generated by technologically advanced analytical instruments such as high-resolution mass spectrometry. I have made my tools easy to use and publicly available, and I have demonstrated in my publications that these computational tools can significantly increase precision and sensitivity to implement mass spectrometry data for diagnostic assay development.
In my latest paper in the journal Analytical Chemistry, I have highlighted that the pregnant women may be exposed to unexpected PFAS, warranting more rigorous biomonitoring of pregnancies using high-resolution mass spectrometry methods and advanced computational tools that I have developed. I will continue to research on advancements of these computational resources and to further advance my skills in computational metabolomics to simplify interpretation of exposomics and metabolomics datasets. These computational improvements can benefit all biomedical fields and health-related projects where high-resolution mass spectrometry is utilized.
What excites you about computational metabolomics research?
Prevalence for many chronic diseases are surging in the United States and new disease prevention strategies are required to address these coming waves of chronic diseases. Gene and environmental factors and their interactions are major determinants for these disease’s phenotypes. Disrupted metabolic pathways have been regularly implicated in chronic disease etiology and progress. Computational metabolomics and exposomics fields deal with advancing the software and database to gain new biological insights to understand mechanisms of chronic diseases. When I see the computational pipelines that I developed are used in biomedical projects which can be translated into medical solutions, I greatly feel that I am contributing positively. I hope to continue learning about the biological background of disease mechanisms of chronic diseases related to all stages of life.
Can you share any interesting experiences/anecdotes about your work with Dr. Dinesh Barupal in general?
I joined Dr. Dinesh Barupal’s new research group, the Integrated Data Science Laboratory for Metabolomics and Exposomics amid COVID-19 pandemic. Dinesh has given me excellent mentorships since joining his lab in August 2020. Through my postdoctoral fellowship, I have learned computational biology, programming with R language, and statistical analysis from Dinesh. Dinesh is incredibly understanding, kind, and supportive with me as I learned how to analyze biological systems during my postdoctoral fellowship. I am so grateful for having learned these new skill sets.
What do you plan to work on after completing your postdoctoral fellowship?
I have a lot of ideas on how to respond to many immediate unsolved problems in the computational biomedical fields. For example, characterizing human exposure to environmental contaminants using high-resolution mass spectrometry instruments to apply precision exposomics methods to improve responses to drugs and therapies through understanding how exposomics factors are affecting drug’s mode of actions. I also have plans for integration of next-generation genomics and exposomics to build multi-aspect models to predict risks of chronic diseases. I have shown my expertise and dedication to excellence in my work and I would love to pursue a scientific career in this field. I am aiming to become a well-known scientist whether in industry or academia to establish a nationally and internationally scientific platform for myself to further expand my research. I hope I can become a mentor in the future to train more scientists to solve biomedical issues.
How did you choose Mount Sinai?
My PhD thesis at Clarkson University (Potsdam, NY) was about discovery of emerging contaminants in Great Lakes fish where I learned a lot about mutual interaction of human and the environment. I wanted to apply my knowledge in environmental toxicology on public health-related projects. My postdoctoral fellowship in the Department of Environmental Medicine and Public Health (EMPH) at Icahn Mount Sinai was an exceptional opportunity for me to also continue my research on studying the effects of environmental contaminants on human populations. EMPH is a pioneer in public health and epidemiological studies.