Predictive Modeling to Define Antiviral Efficacy

Developing computational models to aid in identifying key cellular markers for determining antiviral efficacy in CMV

Full Project Name:Predictive Computational Modeling to Define Antiviral EfficacyPrinciple Investigator:Scott S. Terhune, PhD, Microbiology and ImmunologyCo-Investigator:Ranjan K. Dash, PhD, Biomedical EngineeringAward Amount:$200,000
Award Date
February2017
Project Duration:24 months

Project Description Narrative:


Cytomegalovirus (CMV) is a member of the herpesvirus family and exposure results in a life-long infection. CMV causes severe disease in immunosuppressed patients, is a leading cause of congenital birth defects, and chronic infection is associated with diverse pathologies. Through this award, investigators aim to develop computational models that aid in identifying key cellular markers for determining antiviral efficacy.

Project Updates:


• Documented progress in developing a predictive computational model of the normal CycB-Cdk1 oscillation (mitotic cell cycle) and its dysregulation with different perturbations, including cytomegalovirus (CMV) infection, that more accurately accounts for the oscillating changes occurring during mitosis

• Supported development of students within team's respective programs, introducing students in the Interdisciplinary Program in Biomedical Sciences at MCW to terminology and concepts used in computational biology focusing on mitosis, while students in Marquette University and MCW Department of Biomedical Engineering will be learning methods and concepts in biological sciences focusing on mitosis

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