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Persistent Correlation in Cellular Noise Determines Longevity of Viral Infections.
Abhilasha Batra1, Shoubhik Chandan Banerjee2, Rati Sharma1
1Department of Chemistry, Indian Institute of Science Education and Research (IISER), Bhopal, Madhya Pradesh 462066, India.
The Journal of Physical Chemistry Letters
|August 1, 2022
Summary
COVID-19 infections exhibit slow viral decay due to cellular noise. Our stochastic model shows long-range fluctuations drive this persistent viral dynamics and impact immune response, aligning with patient data.
Area of Science:
- Virology
- Immunology
- Mathematical Biology
Background:
- COVID-19 infection is characterized by slow viral decay, persisting for weeks post-diagnosis.
- The role of inherent cellular variations and noise in this prolonged viral shedding remains understudied.
- Existing models often overlook the impact of stochastic fluctuations on viral dynamics.
Purpose of the Study:
- To investigate the influence of cellular-level variations on the long-lived viral dynamics of COVID-19.
- To develop a theoretical framework for understanding the interplay between immune cells, virus, and stochastic fluctuations.
- To analyze how temporal correlations in fluctuations affect viral persistence and immune response rates.
Main Methods:
- Development of a stochastic non-Markovian mathematical model.
- Simulation of coupled dynamics between immune cells and the SARS-CoV-2 virus.
- Analysis of long-range temporal correlations within the model's fluctuations.
Main Results:
- Long-range temporal correlations in cellular fluctuations are identified as key drivers of persistent viral dynamics.
- These correlations significantly influence the rates at which the immune system responds to infection.
- Model predictions demonstrate strong agreement with experimental viral load data from international COVID-19 patient cohorts.
Conclusions:
- Stochastic fluctuations and their temporal correlations are crucial for understanding the prolonged nature of COVID-19.
- The proposed non-Markovian model provides a valuable tool for studying viral infections with inherent noise.
- Findings offer insights into the complex host-pathogen interactions and immune responses in viral diseases.
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