(533b) Chemical Kinetic Modeling of Intracellular Viral Processes in VSV-Infected Single Cells

Viral infectious diseases, such as Influenza, pose an important health concern. Even a single virus particle can infect a cell, hijack its resources, and manufacture thousands of new virus particles. The production of viruses from infected cells is a complicated process involving a large number of intracellular reactions. Study of these intracellular viral processes can provide us with vital clues that can help us develop new vaccines, antiviral medications, and even help us engineer viruses to treat cancer. Intracellular reactions that depend on small numbers of molecules and the interaction between viral and cellular components result in highly heterogeneous infection outcomes. Single-cell studies have shown that virus yields from individual cells can vary widely, as much as 300-fold [1]. It is possible that a few infected cells produce exceptionally high numbers of virus progeny relative to the average or majority of the infected cells. Behavior of these rare cells may have important long-term consequences towards the progression of the infection [1]. The throughput of single-cell studies can be increased through the use of fluorescent reporter strains of viruses. We have shown that these strains can be used to analyze viral protein production within cells over time, and that viral reporter expression is correlated with virus yields [2]. In this paper, we use quantitative viral reporter measurements from infected single cells and stochastic and deterministic reaction models to study intracellular viral processes.

Baby Hamster Kidney (BHK) cells were infected with a strain of vesicular stomatitis virus (VSV), encoding the gene for DSRed-Express protein, at various multiplicities of infection (MOIs) and then seeded into microwells. Wells containing one cell were identified and the fluorescent expression from these wells analyzed. Overall, we obtain RFP measurements at 30 minute intervals from 500-1000 individual VSV-infected BHK cells for MOI values of 1, 10, 40, and 80. This quantitative data allows us to build chemical kinetic models and estimate the kinetic rate parameters involved.

Such a wide variation in biological systems is often considered intrinsic (or aleatory) [3] and described via stochastic chemical kinetic models [4]. Stochastic chemical kinetic models describe the behavior of reacting systems when the number of molecules are small (1-1000) and the continuum assumption is not suitable. Stochastic reaction modeling involves probabilistically simulating the integer-valued molecule counts instead of deterministically simulating the concentration of reactive species, which is the case in a classical, deterministic reaction modeling approach. In this paper, we developed new chemical kinetic models -- both deterministic and stochastic -- to describe the relevant intracellular viral reproductive processes such as activation, transcription, replication, translation, and degradation. For both kinds of models, we estimate kinetic rate parameters for the available data using statistical techniques such as maximum likelihood estimation, Bayesian inference, and Monte Carlo methods [4]. Finally, we compared the fit of a deterministic reaction model against the fit of a stochastic reaction model to the same data. This analysis is done in order to discover whether the uncertainty described by the data is more aleatory or epistemic [3].

References:

[1] Timm, Andrea and Yin, John. Kinetics of virus production from single cells, Virology, Volume 424, Issue 1, 1 March 2012, Pages 11-17, ISSN 0042-6822, http://dx.doi.org/10.1016/j.virol.2011.12.005.

[2] Jay Warrick, Andrea Timm, Adam Swick, and John Yin, Tools for single-cell kinetic analysis of virus-host interactions, PLoS One, January 2016.

[3] Simeone Marino, Ian B. Hogue, Christian J. Ray, Denise E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, Journal of Theoretical Biology, Volume 254, Issue 1, 7 September 2008, Pages 178-196, ISSN 0022-5193, http://dx.doi.org/10.1016/j.jtbi.2008.04.011.

[4] Gupta, Ankur and Rawlings, James B. Comparison of parameter estimation methods in stochastic chemical kinetic models: Examples in systems biology. AIChE Journal, Volume 60, Issue 4, Pages 1253--1268, ISSN 1547-5905, 2014. http://onlinelibrary.wiley.com/doi/10.1002/aic.14409/abstract