(533b) Chemical Kinetic Modeling of Intracellular Viral Processes in VSV-Infected Single Cells
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)  and described via stochastic chemical kinetic models . 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 . 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 .
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