107. A Computational Model of Adenovirus Infection

Abstract

Computational modeling of biological processes provides two distinct benefits for empirical investigators. First, computational models test the impact of an immense array of variables at a broad range of values rapidly and inexpensively in silico. Second, a computational model teaches scientists about the magnitude of an impact due to changes in values of variables. The goal of this project is to develop a highly flexible, user-friendly, biologically relevant model of DNA virus infection. Adenovirus is used as a comparator for the purpose of establishing states (distinct environments) and for identifying the processes that permit transfer from state to state, the rates of those transitions, and the three-dimensional position of each virus at each time point. States in this model include free extracellular virus, cell-surface bound virus, endosome virus, lysosome virus, cytosolic virus, microtubule-associated virus, microtubule-organizing center-associated virus, nuclear bound virus, and inactivated virus. Rates governing transitions between states include diffusion rates, binding and dissociation rates, and microtubule-associated motility, among others. The model employs a game controller to introduce stochastic variability around the chosen rate ensuring that no two iterations of the model give the same dataset. Outputs from the model include comma-separated-value (.csv) data files that are amenable to spreadsheet analysis, graphs showing the relative population of viruses in each state (hundreds of viruses can be used in each infection), and three-dimensional images of the infected cell. An initial characterization of the model demonstrates the veracity of several individual elements. For example, with a pre-set half time for endocytic internalization of bound virus from the cell surface, one would predict that 87.5% of bound virus at time zero would enter the cell in 3 half times (7.5 minutes). Three iterations of the model predicted internalization of 84.2 +/- 13.9% (mean +/- sd) of bound virus over that time period. Similarly, modeling diffusion of vectors in the cytoplasm is an important element in understanding the contribution of microtubule-associated motility to DNA delivery to the nucleus. A prior study of adenovirus diffusion in aqueous medium indicated a diffusion coefficient of 3.67 +/- 0.03 × 10^-8 cm2sec-1 (Oliver et al., 1976, BBA 437:589). The cytoplasm is known to impede diffusion, especially for particles like adenovirus that are larger than 50 nm (Seksek et al., 1997, JCB 138:131), leading to a reduction of the diffusion coefficient by at least two orders of magnitude. Using conditions that allow for a total linear translocation of 1000 nm per sec with a stochastic re-setting of direction 200 times per sec, three iterations of the model predict a diffusion coefficient of 1.80 +/-0.48 × 10^-11 cm2sec-1 (mean +/- se), 204x less than diffusion of adenovirus in water. The model will be validated against published experimental data and will incorporate point-and-click selection of infection conditions. In summary, the model provides a user-friendly, visual and quantitative tool for performing computational studies of viral infection.