Forecasting the timeframe of 2019-nCoV and human cells interaction with reverse engineering

Area of Science:

• Virology
• Computational Biology
• Mathematical Modeling

Background:

• The SARS-CoV-2 pandemic poses a significant global health threat, necessitating effective control strategies.
• Understanding the complex dynamics of SARS-CoV-2 spread is crucial for developing interventions.
• Currently, no specific vaccines or approved medical treatments are available for SARS-CoV-2.

Purpose of the Study:

• To investigate the molecular mechanism of SARS-CoV-2 infection, focusing on the interaction between viral S protein, ACE2 receptor, and B0AT1 protein.
• To develop a mathematical model simulating cell-virus interactions and predicting the incubation period of COVID-19.
• To explore the potential of delay-based control strategies informed by a theoretical framework.

Main Methods:

• A mathematical model was developed based on cell-virus interaction principles.
• The interaction between the SARS-CoV-2 S protein and the ACE2 receptor was mimicked using Hill function formalism.
• A delay differential equation model was solved numerically, with parameter values obtained via the Markov Chain Monte Carlo (MCMC) algorithm.

Main Results:

• A delay differential equation model was established to illustrate the dynamics of target cells, infected cells, and SARS-CoV-2.
• Numerical computations demonstrated key parameters and coefficients influencing viral dynamics.
• The model provides thresholds and forecasts that can aid future experimental studies and control strategies.

Conclusions:

• Control strategies incorporating time delays show promise for managing SARS-CoV-2 spread.
• The Hill function formalism offers an interpretable and cost-effective approach for developing control strategies.
• The developed theoretical framework can enhance the understanding and application of control strategies against viral infections.