Computational systems neurobiology with an emphasis on the complicated systems of neuronal, glial, and molecular interactions that underlie neurological diseases
Experimental neuroscience has produced an immense dataset of findings on the molecular, synaptic, cellular, network, and behavioral levels. Hidden in that dataset may be the keys to understanding neurological and mental disorders such as Alzheimer and Parkinson disease, post-traumatic stress disorder, depression, and many others, but the sheer size and complexity of the dataset poses a daunting barrier to understanding. My lab is dedicated to developing and using computational procedures to represent those findings in their hundreds (ultimately thousands) and to explore what they mean in the aggregate. Our goal is to gain new insights into complex, multilevel neurobiological processes and to propose new strategies, such as multidrug therapies, to treat neurological diseases.
Anastasio TJ (2011) Data-driven modeling of Alzheimer disease pathogenesis. Journal of Theoretical Biology 290:60-72.
Anastasio TJ (in press) Exploring the contribution of estrogen to amyloid-beta regulation: a novel multifactorial computational modeling approach. Frontiers in Experimental Pharmacology and Drug Discovery.
Anastasio TJ (in review) Computational search for hypotheses concerning the endocannabinoid contribution to the extinction of fear conditioning.