A Parameter Estimation Method for Multiscale Models of Hepatitis C Virus Dynamics

Area of Science:

• Mathematical Biology
• Computational Virology
• Systems Biology

Background:

• Mathematical models, particularly those involving differential equations, necessitate accurate parameter estimation.
• Sophisticated multiscale models, such as those for hepatitis C virus (HCV) dynamics using partial differential equations (PDEs), present unique parameter estimation challenges.
• Existing methods like analytical approximations, transformation to ordinary differential equations (ODEs), or black-box numerical solutions have limitations in scope, parameter integrity, or user control.

Purpose of the Study:

• To develop a novel, user-controlled strategy for parameter estimation in multiscale mathematical models.
• To overcome the limitations of existing parameter estimation methods for complex models like those describing viral dynamics.

Main Methods:

• Directly manipulating multiscale model equations to prepare them for parameter estimation.
• Developing a fully coded, user-controlled parameter estimation method.
• Utilizing a user-friendly simulator to demonstrate the new method's application.

Main Results:

• A new strategy for parameter estimation in multiscale models has been successfully developed and described.
• The method allows for direct work on model equations, offering greater user control.
• Illustrations using a simulator demonstrate the method's practical application and adaptability.

Conclusions:

• The developed strategy offers a more flexible and controlled approach to parameter estimation for multiscale models.
• This method can be adapted for multiscale models of other viruses beyond hepatitis C.
• Enhanced control over parameter estimation improves the reliability and applicability of complex mathematical models in virology and beyond.