Global sensitivity analysis of a single-cell HBV model for viral dynamics in the liver

Abstract

The predictive accuracy of mathematical models representing anything ranging from the meteorological to the biological system profoundly depends on the quality of model parameters derived from experimental data. Hence, robust sensitivity analysis (SA) of these critical model parameters aids in sifting the influential from the negligible out of typically vast parameter regimes, thus illuminating key components of the system under study. We here move beyond traditional local sensitivity analysis to the adoption of global SA techniques. Partial rank correlation coefficient (PRCC) based on Latin hypercube sampling is compared with the variance-based Sobol method. We selected for this SA investigation an infection model for the hepatitis-B virus (HBV) that describes infection dynamics and clearance of HBV in the liver [Murray & Goyal, 2015]. The model tracks viral particles such as the tenacious and nearly ineradicable covalently closed circular DNA (cccDNA) embedded in infected nuclei and an HBV protein known as p36. Our application of these SA methods to the HBV model illuminates, especially over time, the quantitative relationships between cccDNA synthesis rate and p36 synthesis and export. Our results reinforce previous observations that the viral protein, p36, is by far the most influential factor for cccDNA replication. Moreover, both methods are capable of finding crucial parameters of the model. Though the Sobol method is independent of model structure (e.g., linearity and monotonicity) and well suited for SA, our results ensure that LHS-PRCC suffices for SA of a non-linear model if it is monotonic.

Assessing the mechanism of citywide test-trace-isolate Zero-COVID policy and exit strategy of COVID-19 pandemic

“…The ranges used in the LHS are reported in Table 2 . The parameters with a PRCC of magnitude greater than or equal to 0.5 were considered significant in the cumulative cases and isolated people [ 41 ].…”

“…The number of confirmed and isolated individuals at day T 1 5 Data [31] Parameters for COVID- 19 2. The parameters with a PRCC of magnitude greater than or equal to 0.5 were considered significant in the cumulative cases and isolated people [41].…”

Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada

“…Conversely, PRCC values below 0 indicate that the parameter and the output are negatively correlated, indicating that as the parameter value increases, the outcome decreases (and vice versa). Parameters with an absolute PRCC value greater than 0.5 are considered significant [37,38]. Ranges of sampled parameters are shown in Table SI2.…”

Illuminating HBV with multi-scale modeling

“…Although animal models and in vitro experiments cannot approach the level of precision for such a multi-scale mathematical scheme, parameters for infection dynamics, transport and activity must necessarily derive from measurements or at least sit in realistic ranges. Sensitivity analysis determining the dominant influence of parameters within the framework of these multi-scale mathematical models can inform fruitful directions for experimental focus (Ali et al, 2021). Fortunately, extraordinary advances in experimental work with spatial proteogenomics and transcriptomics are encouraging; detecting previously unavailable distributions for numerous hepatic activities can naturally inform the multi-scale model described here.…”