Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic Model

Abstract

Mathematical modelling of infection processes in cells is of fundamental interest. It helps to understand the SARS-CoV-2 dynamics in detail and can be useful to define the vulnerability steps targeted by antiviral treatments. We previously developed a deterministic mathematical model of the SARS-CoV-2 life cycle in a single cell. Despite answering many questions, it certainly cannot accurately account for the stochastic nature of an infection process caused by natural fluctuation in reaction kinetics and the small abundance of participating components in a single cell. In the present work, this deterministic model is transformed into a stochastic one based on a Markov Chain Monte Carlo (MCMC) method. This model is employed to compute statistical characteristics of the SARS-CoV-2 life cycle including the probability for a non-degenerate infection process. Varying parameters of the model enables us to unveil the inhibitory effects of IFN and the effects of the ACE2 binding affinity. The simulation results show that the type I IFN response has a very strong effect on inhibition of the total viral progeny whereas the effect of a 10-fold variation of the binding rate to ACE2 turns out to be negligible for the probability of infection and viral production.

Stochastic Modelling of HIV-1 Replication in a CD4 T Cell with an IFN Response

“…The efficiency of an HIV-1 infection can be characterised by the ratio of the total viral progeny to the number of free virions infecting the cell (MOI). We define this value as the life cycle efficiency, similar to [ 43 ]: The estimated values of the life cycle efficiency are indicated in the corresponding rows of Table 2 .…”

Predicting the Cross-Coordinated Immune Response Dynamics in Sars-Cov-2 Infection: Implications for Disease Pathogenesis

“…Taking the ranges t l.c. ∈ (7, 24) h, V MOI ∈ (1, 10) [30,38], and f D ∈ (0.1, 0.5), C * ∈ (10 9 , 10 10 ) cells [23], we arrive to the estimate σ ∈ (2…”

“…For the rate of SARS-CoV-2 virions secretion per infected epithelial cell, ν, we set the initial guess ν = 130 day −1 and admissible range (10, 1000) day −1 based on our previous experience of modelling SARS-CoV-2 replication cycle [30,38].…”

Exploring the therapeutic potential of defective interfering particles in reducing the replication of SARS-CoV-2

“…( 4)-( 27) can be generalised to a stochastic one formulated as a discrete-state continuous-time Markov chain (DSCT MC). The stochastic model allows one to account for integer-valued variables, to obtain probability distributions rather than mean field estimates for the variables of interest, and to compute the probabilities of productive cell infection at low MOI [58]. It is convenient to estimate model parameters for the system of ODEs and then, with a calibrated deterministic system, and a defined Markov chain model, perform stochastic simulations making use of Monte Carlo methods.…”