
Contact Information
524 Burrill Hall
407 S. Goodwin Ave
Urbana, IL 61801
Research Interests
Research Topics
Computational Biology, Neurobiology
Disease Research Interests
Drug Discovery, Neurological and Behavioral Disorders
Research Description
Multilevel modeling of neurobiological systems in health and disease.
Many of the most interesting and important neurobiological phenomena, as well as the pathological processes underlying neurological diseases, involve interactions at multiple levels. For example, certain forms of Alzheimer Disease result from mutations in the genes that code for the proteins that process the beta-amyloid peptide, the build-up of which results in the dysfunction and death of neurons, which in turn lead to failure of the neural circuits and brain regions that mediate memory and cognition. Other multilevel processes are implicated in other neurological and psychological disorders. Our work concerns the computational modeling of multilevel neurobiological process, with a current focus on Alzheimer and other neurodegenerative diseases, mood disorders including depression and anxiety, and eating disorders. By representing experimental findings formally as declarations in a computer program, the multilevel physiology and pathophysiology of various neurobiological processes can be explored through simulation and analysis, leading to experimentally testable predictions and new perspectives on possible pharmacological interventions.
Education
B.Sc. 1980 McGill University
Ph.D. 1986 University of Texas, Galveston
Postdoc. 1988 John Hopkins University
Additional Campus Affiliations
Associate Professor Emeritus, Molecular and Integrative Physiology
External Links
Recent Publications
Anastasio, T. J. (2021). Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning. Processes, 9(2), 1-17. [264]. https://doi.org/10.3390/pr9020264
Camacho, M. B., Vijitbenjaronk, W. D., & Anastasio, T. J. (2020). Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant. European Neuropsychopharmacology, 31, 86-99. https://doi.org/10.1016/j.euroneuro.2019.11.003
Anastasio, T. J. (2019). Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model. Journal of Alzheimer's Disease, 70(1), 287-302. https://doi.org/10.3233/JAD-190144
Camacho, M. B., Vijitbenjaronk, W. D., & Anastasio, T. J. (2019). Computational analysis of therapeutic neuroadaptation to chronic antidepressant in a model of the monoaminergic neurotransmitter and stress hormone systems. Frontiers in Pharmacology, 10, [1215]. https://doi.org/10.3389/fphar.2019.01215
Anastasio, T. J., Barreiro, A. K., & Bronski, J. C. (2017). A geometric method for eigenvalue problems with low-rank perturbations. Royal Society Open Science, 4(9), [170390]. https://doi.org/10.1098/rsos.170390