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S2M: A Stochastic Simulation Model of Poliovirus Genetic State Transition

Journal Article · · Bioinformatics and Biology Insights
DOI:https://doi.org/10.4137/bbi.s38194· OSTI ID:1771034
 [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
+ Show Author Affiliations
Modeling the molecular mechanisms that govern genetic variation can be useful in understanding the dynamics that drive genetic state transition in quasispecies viruses. For example, there is considerable interest in understanding how the relatively benign vaccine strains of poliovirus eventually revert to forms that confer neurovirulence and cause disease (ie, vaccine-derived poliovirus). This report describes a stochastic simulation model, S2M, which can be used to generate hypothetical outcomes based on known mechanisms of genetic diversity. S2M begins with predefined genotypes based on the Sabin-1 and Mahoney wild-type sequences, constructs a set of independent cell-based populations, and performs in-cell replication and cell-to-cell infection cycles while quantifying genetic changes that track the transition from Sabin-1 toward Mahoney. Realism is incorporated into the model by assigning defaults for variables that constrain mechanisms of genetic variability based roughly on metrics reported in the literature, yet these values can be modified at the command line in order to generate hypothetical outcomes driven by these parameters. To demonstrate the utility of S2M, simulations were performed to examine the effects of the rates of replication error and recombination and the presence or absence of defective interfering particles, upon reaching the end states of Mahoney resemblance (semblance of a vaccine-derived state), neurovirulence, genome fitness, and cloud diversity. Simulations provide insight into how modeled biological features may drive hypothetical outcomes, independently or in combination, in ways that are not always intuitively obvious.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1771034
Report Number(s):
LLNL-JRNL--683469; 807605
Journal Information:
Bioinformatics and Biology Insights, Journal Name: Bioinformatics and Biology Insights Vol. 10; ISSN 1177-9322
Publisher:
SAGECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Mahoney
Sabin
genetic state transition
genome evolution
modeling
picornavirus
recombination
replication
simulation