HCV Spread Kinetics Reveal Varying Contributions of Transmission Modes to Infection Dynamics

Abstract

The hepatitis C virus (HCV) is capable of spreading within a host by two different transmission modes: cell-free and cell-to-cell. However, the contribution of each of these transmission mechanisms to HCV spread is unknown. To dissect the contribution of these different transmission modes to HCV spread, we measured HCV lifecycle kinetics and used an in vitro spread assay to monitor HCV spread kinetics after a low multiplicity of infection in the absence and presence of a neutralizing antibody that blocks cell-free spread. By analyzing these data with a spatially explicit mathematical model that describes viral spread on a single-cell level, we quantified the contribution of cell-free, and cell-to-cell spread to the overall infection dynamics and show that both transmission modes act synergistically to enhance the spread of infection. Thus, the simultaneous occurrence of both transmission modes represents an advantage for HCV that may contribute to viral persistence. Notably, the relative contribution of each viral transmission mode appeared to vary dependent on different experimental conditions and suggests that viral spread is optimized according to the environment. Together, our analyses provide insight into the spread dynamics of HCV and reveal how different transmission modes impact each other.

Inferring cell motility in complex environments with incomplete tracking data

“…Especially if the heterogeneity and complexity of the underlying system is high (e.g. multiple model parameters, heterogeneous environment, small cell numbers due to cell track loss) the use of large particle numbers is suggested, as it is generally done for ABM approaches [26, 27]. Given our simulated scenarios, we found that a particle number between m = 200-1000 combined with a subsampling depth of n = 50-100 showed good results for the analysis of homogenous cell populations.…”

“…These environments allow for extrapolation from short-term and spatially limited quantitative information towards long-term and large cell population dynamics. While most studies relied on manual and qualitative adaptations of the simulation models to inform model parameters, nowadays advanced methods allow for automatic data-driven parameter inference for complex individual cell based models using different types of data [6, 25, 26]. For example, the FitMultiCell -pipeline based on the integration of Morpheus [17] with the approximate Bayesian computation method pyABC [27, 28] allows parameter inference and simulation of multicellular systems as e.g.…”

Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data

“…As outliers we considered interchanging in total 20 data points in the observables' dynamic regimes. Agent-based models describing biological processes such as pathogen spread or tissue growth have recently been frequently analyzed using ABC methods [Durso-Cain et al, 2021, Imle et al, 2019.…”

“…Consequently, methods have been developed that aim to detect and remove outliers, prior to and independent of the inference method used [Ben-Gal, 2005, Hodge and Austin, 2004, Niu et al, 2011. However, for noise-corrupted high-dimensional or highly structured data with few replicates, which are common in biological problems and also as applications of ABC [Durso-Cain et al, 2021, Jagiella et al, 2017, such methods may be unreliable, and the complete removal of points that are not actually outliers can increase uncertainty [Motulsky and Christopoulos, 2003]. To circumvent this, estimators that are robust in the presence of outliers have been developed, using heavy-tailed distributions [Berger et al, 1994, Fernández and Steel, 1999, Huber et al, 1964, Tarantola, 2005 or pseudo-likelihoods with robust loss functions or divergences [Basu et al, 1998, Chérief-Abdellatif and Alquier, 2020, Jewson et al, 2018.…”

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

“…For multi-scale models of multi-cellular processes a key challenge is parameter estimation, the process in which values for the unknown model parameters are determined by fitting the model simulations to experimentally observed data. Parameter estimation is necessary to obtain quantitative models of processes, to analyse processes, to compare competing hypotheses about processes, and to predict the dynamics of processes (e.g., in response to perturbations) [Durso-Cain et al, 2021, Imle et al, 2019, Jagiella et al, 2017, MacLean et al, 2014, Toni et al, 2011]. Systematic, rigorous and uncertainty-aware parameter estimation is only just becoming accessible for multi-scale models with advanced methods and growing computational resources.…”