Biography
Tommy W. Terooatea is an Assistant Teaching Professor at Brigham Young University and Director of BYU's Genomics and Bioinformatics Center. He also holds a courtesy appointment in the BYU School of Medicine. His research asks whether cell behavior follows a grammar, a set of recurring mathematical rules governing how regulatory networks of genes, proteins and chromatin change cell state, and how those rules change with age and disease. His lab develops the mathematics and computational methods to measure these dynamics from single-cell data, with immune aging as its main test system.
Originally from Raiatea in French Polynesia, Tom earned a BS in Biochemistry from BYU–Hawaii and a PhD in Biological Chemistry from the University of Utah, where he studied the epigenetic regulation of the transcription factor ZBTB33 in cancer cells. He was then a research scientist at the RIKEN Center for Integrative Medical Sciences in Yokohama, Japan, serving as lead bioinformatician in the Laboratory for Cellular Epigenomics. From 2021 to 2024 he was a senior computational biologist at KOTAI Biotechnologies in Japan, leading multi-omic and immune-repertoire analysis for vaccine and antibody discovery with pharmaceutical partners.
At BYU he teaches Systems Biology and Introduction to Bioinformatics.
Research Interests
My lab asks whether cell behavior follows a grammar: a small set of recurring mathematical rules that govern how regulatory networks of genes, proteins and chromatin change cell state, and how those rules change with age and disease.Cells do not choose from unlimited possibilities. Across tissues and organisms, the same kinds of dynamics keep appearing: switches between stable states, cycles, thresholds, gradual drift, and recovery after stress. We think these patterns may be the vocabulary of cell behavior, and the ways they combine and follow one another its syntax. If that is right, regulation at the systems level can be described by rules that are general, composable and predictive, much as a few physical laws describe many different systems. If it is wrong, we want to show that clearly and say exactly where it fails.
Finding these rules is a measurement problem as much as a biological one. A single-cell snapshot shows which states exist but not which way cells are moving, how fast, or in what order. My group develops the mathematics and computational methods to recover those missing properties, using time, internal clocks, kinship between cells and perturbation, and builds the tools to apply them. scJDO, our framework for estimating local regulatory operators from single-cell data, is the first of these.
Immune aging is our main test system. Immune systems age at different rates, and some people stay resilient while others become frail. We want to know whether that difference reflects a change in the grammar itself, not just in which cell states are present. Our goal is a quantitative description of cell regulation that can be measured, compared across individuals, and used to predict how cells and tissues will respond.