A detailed model and Monte Carlo simulation for predicting DIP genome length distribution in baculovirus infection of insect cells

Das, Ashok and Dutta, Soumajit and Sen, Moumita and Saxena, Abha and Kumar, Jitendra and Giri, Lopamudra and Murhammer, David W. and Chakraborty, Jayanta (2021) A detailed model and Monte Carlo simulation for predicting DIP genome length distribution in baculovirus infection of insect cells. Biotechnology and Bioengineering, 118 (1). pp. 238-252. ISSN 0006-3592

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Baculoviruses have enormous potential for use as biopesticides to control insect pest populations without the adverse environmental effects posed by the widespread use of chemical pesticides. However, continuous baculovirus production is susceptible to DNA mutation and the subsequent production of defective interfering particles (DIPs). The amount of DIPs produced and their genome length distribution are of great interest not only for baculoviruses but for many other DNA and RNA viruses. In this study, we elucidate this aspect of virus replication using baculovirus as an example system and both experimental and modeling studies. The existing mathematical models for the virus replication process consider DIPs as a lumped quantity and do not consider the genome length distribution of the DIPs. In this study, a detailed population balance model for the cell-virus culture is presented, which predicts the genome length distribution of the DIP population along with their relative proportion. The model is simulated using the kinetic Monte Carlo algorithm, and the results agree well with the experimental results. Using this model, a practical strategy to maintain the DIP fraction to near to its maximum and minimum limits has been demonstrated.

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IITH Creators:
IITH CreatorsORCiD
Giri, Lopamudrahttp://orcid.org/0000-0002-2352-7919
Item Type: Article
Uncontrolled Keywords: algorithm; article; Baculoviridae; cell culture; defective virus; human; insect cell; Monte Carlo method; nonhuman; virus culture; virus replication;Biopesticides; Chemical pesticides; Detailed modeling; Genome length; Insect pest populations; Kinetic Monte Carlo; Population balance modeling; Virus replication
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 09 Jul 2021 05:20
Last Modified: 09 Jul 2021 05:20
URI: http://raiith.iith.ac.in/id/eprint/8185
Publisher URL: http://doi.org/10.1002/bit.27566
OA policy: https://v2.sherpa.ac.uk/id/publication/14548
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