Computational modeling of protracted HCMV replication using genome substrates and protein temporal profiles

Area of Science:

• Virology
• Computational Biology
• Systems Biology

Background:

• Human cytomegalovirus (HCMV) causes significant illness in immunocompromised individuals.
• The HCMV lytic cycle, involving viral DNA replication and protein expression, drives infection's clinical effects.
• Understanding the complex temporal dynamics of HCMV lytic replication is crucial.

Purpose of the Study:

• To develop a mechanistic computational model simulating HCMV's multiplicity of infection (MOI)-dependent vDNA kinetics and late lytic replication.
• To validate the model's predictive capabilities using experimental data.
• To computationally analyze regulatory mechanisms influencing HCMV replication.

Main Methods:

• Developed an MOI-dependent mechanistic computational model using in-house experimental data.
• Validated model predictions against post hoc experimental data.
• Performed computational analysis of regulatory mechanisms and simulated vDNA kinetics across various MOIs.

Main Results:

• The model accurately simulates vDNA kinetics and late lytic replication.
• Identified saturation behavior at high MOIs and inefficient replication at low MOIs.
• Determined an optimal MOI range (0.382–0.688 IU/fibroblast) for maximal virus production.

Conclusions:

• The developed computational model provides a data-driven framework for simulating lytic HCMV infection.
• Model predictions offer insights into cellular machinery limitations and minimum input requirements for infection.
• The model is expandable for incorporating additional regulatory mechanisms.