Mathematical modelling of baculovirus infection process: Kinetic parameter estimation

Singh, Suraj Kumar and Giri, Lopamudra (2018) Mathematical modelling of baculovirus infection process: Kinetic parameter estimation. Masters thesis, Indian Institute of Technology Hyderabad.

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Abstract

Although there are several mathematical models present for baculovirus infection, the specific functions for insect cell growth and cell death during infection processes remain unknown. Specifically, it is challenging to identify the most suitable model from a large set of plausible models and estimate the kinetic parameters to account for the day to day variability present in the infection experiments. In this context, identification of an unstructured model that can predict the day to day variability in cell growth and cell viability can be useful in determining the optimal operating conditions in fermenters at industrial scale. The major objectives of the present work were to develop a model screening framework that can be used to select the best model and identify the growth and death mechanisms during viral infection through non-linear programming. We then constructed a series of plausible models based on system of ordinary differential equations and performed the model selection using experimental data obtained from shaker flasks. The proposed scheme was tested for selecting the model for uninfected cell growth profiles. The objective function used was the root mean square error between the predicted values and experimental data points obtained from triplicate dataset. The computational scheme was validated using two types of virus, the WT AcMNPV and stabilized AcMNPV. Additionally, we propose a numerical scheme to simulate the cell growth and cell viability during viral passaging. The kinetic parameters were estimated in case of growth of uninfected cells, cells infected with WT virus as well as stabilized AcMNPV. The result shows that Monods equation fits the best for insect cell growth without infection and infection with WT AcMNPV. Whereas, the Contois model fits the best for the stabilized virus. The simulated results also indicate that the day to day variability in cell growth and cell viability profile can be explained through the variation in the specific growth rate and the death rate. The estimated kinetic parameters indicate that the growth and death parameters undergo specific modifications during the passaging of viruses associated to infection process. Additionally, we propose an integrated model for the infection process that simulates the DNA replication, mRNA and protein expression as well as polyhedra production. Specifically, we present the comparison between the unstructured model and the structured integrated model with respect to accuracy and computation time. Current study provides a predictive framework that has a potential application for large scale production of baculovirus.

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