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 EfficacyPrincipal 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.

Outcomes & Lessons Learned:

Defined the base computational model of the human mitotic cell cycle, which simulates temporal changes in 12 different mitotic proteins and associated protein complexes existing in 27 different states using 15 major mitotic interacting reactions and 26 ordinary differential equations
Model parameters were defined and shown through testing to reproduce the cardinal features of human mitosis, as determined experimentally by numerous laboratories
The model has been used to help the research team understand why CMV targets specific factors during mitosis
Using computer simulations, the research team observed that virus-mediated disruption is necessary to sustain a unique mitotic collapse defined by stable cellular kinase levels and is consistent with known mechanisms of viral egress
The research team is finishing studies defining the impact of bortezomib on CMV replication in vitro with the goal of linking the experimental data with the computer simulations
Findings are being prepared for a manuscript submission to the journal PLoS Computational Biology
Knowledge gained from this project has been included in course materials for the Marquette University and MCW Biomedical Engineering Graduate Program
Findings from this project were disseminated at local, national, and international meetings and conferences, and the research team continues to educate other faculty and students about how the general process to create the base computational model is applicable to most biological processes

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