Stochastic vs. deterministic modeling of intracellular viral kinetics

Prior research has shown that deterministic models can describe virus growth using differential equations. However, these models may miss fluctuations when only a few virus particles initiate infection. It was already known that deterministic approaches assume continuous and predictable changes in viral components. No prior work had resolved how stochastic models might differ in predicting viral kinetics at low multiplicities of infection. This gap motivated the need to compare deterministic and stochastic modeling approaches. Established knowledge includes how viral replication involves transcription, translation, and assembly processes. That uncertainty drove the investigation into whether random fluctuations could alter predicted outcomes. The study aimed to explore whether modeling approaches could yield different insights into viral growth dynamics.

The aim was to compare deterministic and stochastic modeling of intracellular viral kinetics. The specific problem addressed was whether random fluctuations in low multiplicity of infection scenarios could alter predicted outcomes. The motivation stemmed from the observation that a single virus particle may initiate infection. This uncertainty could affect predictions of viral growth and persistence. The study sought to determine if deterministic models accurately represent viral behavior at low MOI. The researchers proposed to develop a simple model of a generic virus's intracellular kinetics. They wanted to test whether stochastic simulations could reveal different dynamics than deterministic ones. The goal was to understand how modeling approaches influence predictions of viral persistence.

The researchers developed a simple model of a generic virus's intracellular kinetics. They implemented the model using both deterministic and stochastic approaches. The model included reactions for synthesizing and depleting viral nucleic acids and proteins. Linear stability analysis was applied to the deterministic model to identify nodes. Stochastic simulations were run to observe fluctuations in viral component levels. The team compared transient kinetics and steady-state levels between models. They focused on low multiplicities of infection where few virus particles initiate infection. The model allowed for individual simulation runs to access unstable nodes in some cases.

Linear stability analysis of the deterministic model revealed two nodes: one stable and one unstable. Stochastic simulations showed individual runs could access and remain at the unstable node. Deterministic and averaged stochastic simulations showed different transient kinetics. Stochastic simulations produced different steady-state levels of viral components at low MOI. A bimodal population distribution of viral components was observed in low MOI simulations. This distribution suggested the existence of a low-level infected subpopulation of cells. The researchers propose this subpopulation could act as a viral reservoir. These findings suggest stochastic models may better capture viral persistence mechanisms.

The authors propose that stochastic models may yield different predictions than deterministic ones at low MOI. They suggest that individual simulations can access unstable nodes not predicted by deterministic models. The observed bimodal distribution implies a subpopulation of cells with low-level infection. This subpopulation could act as a viral reservoir, according to the authors. The study suggests that modeling approaches influence predictions of viral persistence. The researchers propose that stochastic fluctuations may be important in early infection stages. They suggest that deterministic models may miss key dynamics at low MOI. The findings suggest that modeling approaches should consider stochastic effects in certain scenarios.