Dr. Steven A. Garan is a scientist known for his interdisciplinary contributions to bioinformatics, systems biology, and aging research. He serves as Director of Bioinformatics at the Center for Research & Education on Aging (CREA) and is affiliated with University of California, Berkeley and the Lawrence Berkeley National Laboratory, where his work focuses on understanding the biological mechanisms of aging through computational modeling, advanced imaging, and systems-level analysis. Throughout his career, he has sought to bridge experimental biology with quantitative science, helping researchers better understand how complex biological systems change over time.
Dr. Garan established the field of “Phenomics” in 1996. Phenomics is the large-scale study of observable traits—such as physical characteristics, metabolism, behavior, cellular function, and aging-related changes—and how they are shaped by genetics, environment, and lifestyle. While genomics focuses on genes, phenomics examines how those genes are actually expressed in real biological systems, helping explain why organisms with similar genetic information can develop very different outcomes. A classic illustration is the caterpillar-to-butterfly transformation: the same underlying genetic material produces dramatically different forms and functions at different stages of development, showing how phenotype changes over time. Using tools like bioinformatics, advanced imaging, artificial intelligence, and computational modeling, phenomics allows scientists to measure and analyze these complex changes across entire organisms and populations. It plays a key role in personalized medicine, systems biology, disease research, drug development, and aging studies by linking genetic data to real-world biological function and health outcomes.
One of Dr. Garan’s most recognized contributions is his role in developing the Automated Imaging Microscope System (AIMS) during his time at the University of California, Berkeley. This high-throughput imaging platform enabled researchers to quantify cellular structures with greater precision and provided new insights into age-related biological changes. Working alongside neuroendocrinology laboratories, he studied how caloric restriction affects receptor expression in mouse brain tissues, contributing to broader understanding of metabolism, hormone regulation, and longevity. His research demonstrated how computational methods and imaging technologies could uncover subtle biological patterns associated with aging.
Over the course of his career, Dr. Garan has authored or co-authored approximately two dozen scientific publications spanning neuroendocrine aging, immune modeling, glucose metabolism, hormonal dynamics, and computational simulations of biological systems. His work often combines biology, mathematics, and computer science to model how signaling networks and physiological systems evolve with age. He has also collaborated internationally on longevity research, including projects with researchers at the Universidade Federal de Juiz de Fora in Brazil. These collaborations produced mathematical models of hormonal cycles and age-related disease vulnerability, exploring computational approaches as alternatives to some in vivo experimentation.
Beyond his own research accomplishments, Dr. Garan is recognized for mentoring students and early-career scientists in bioinformatics, mathematical modeling, and translational biology. His work developing technologies such as AIMS and Internet-of-Medical-Things methods has helped improve the reproducibility and practical application of biological data in both research and clinical settings. Across his scientific, technological, and educational efforts, Dr. Garan has consistently pursued a central goal: advancing understanding of human aging while contributing to strategies that may extend healthy lifespan and improve quality of life.