Daniel Forger, PhD
Professor of Mathematics, Research Professor of Computational Medicine and Bioinformatics at the Michigan Institute for Data Science; Core Faculty, University of Michigan, Ann Arbor
Dr. Daniel Forger’s career has focused on using mathematical techniques to understand biological rhythms. The models he has developed for circadian timekeeping have made predictions that have been tested by many experimental groups. Although most of his training is in mathematics, he was part of a NIH training grant during his PhD and did experiments during his post-doc. Dr. Forger’s interest in mathematical modeling was sparked by a Westinghouse project in high school simulating the electrical behavior of the heart with Charles Peskin. Since then, he has worked on many levels of biology, from detailed biochemistry, through electrophysiology to human behavior. He typically trains students and post-doctoral scientists in a co-mentoring format, where they get a strong background both in mathematics and the biological sciences.
Dr. Forger brings over 20 years of experience in biological mathematical modeling to this multi-disciplinary team. His dedication to the understanding effective prediction of mood patterns using mathematical analysis may prove helpful to both patient and healthcare providers. Recently, his team developed a smartphone app tracking circadian rhythms that have been installed ~200,000 times. He also coordinated an international project studying how circadian timekeeping affects mood funded by HFSP. His team’s discoveries about GABA affecting seasonal affective disorder led to two published papers this year in PNAS. Their work on the effects of GSK (a key target of lithium) discover mechanisms of how circadian rhythms control mood. Dr. Forger also has extensive experience working directly with rhythmic data, making predictions about biological rhythms and developing methodology for studying rhythms.