Our long-term objective is to understand how interactions between genomes and their environments influence the growth of simple organisms. We focus on the genomes of viruses because they carry a small number of genes, essential functions of these genes are often known, and the stages of the virus growth cycle have frequently been well characterized. Viruses also cause many human diseases including, for example, AIDS, influenza, cancer, SARS, and the common cold. Moreover, natural and engineered viruses have potential applications as vaccines and anti-cancer therapeutics. We propose in the first stage of this project to advance a quantitative and integrative understanding of vesicular stomatitis virus (VSV) by building a model of its growth. VSV ranks among the best-characterized viruses.
Specific aims are to: (1) develop a data-driven intracellular computational model for the one-cycle growth of VSV, (2) evaluate the model using experimental data from a panel of recombinant VSV strains, and (3) employ the model to predict how gene order affects VSV growth. In natural host organisms viral infections span beyond one-cycle of growth, they spread spatially, and different infected cells produce different levels of virus. The second stage of the project will begin to address these realities by expanding our initial virus model to: (A) simulate the spatial spread of virus over many generations, and (B) examine how stochastic effects influence VSV one-cycle growth. Specifically, in project (A) we will develop a computational model that couples VSV growth with its spatial spread over many infection cycles. We will perform experiments to enable this model to incorporate and account for experimentally observed inhibitory effects of interferon-mediated cell-cell signaling on VSV growth and spread. In project (B) we will carry out single-cell experimental measurements of infection intermediates and production of virus progeny. At the same time, we will develop a stochastic model of VSV one-cycle growth to interpret these data and assess the role of stochastic effects on virus production by single infected cells. All computer simulations will be solved using the open-source numerical software GNU Octave (www.octave.org), an interactive system for numerical computations that has a growing worldwide user base. Data and software from this project, including a new method for parameter estimation for stochastic models, will be disseminated through a project-dedicated website. ? ? ?
| Yin, John; Redovich, Jacob (2018) Kinetic Modeling of Virus Growth in Cells. Microbiol Mol Biol Rev 82: |
| Timm, Collin; Akpinar, Fulya; Yin, John (2014) Quantitative characterization of defective virus emergence by deep sequencing. J Virol 88:2623-32 |
| Timm, Andrea; Yin, John (2012) Kinetics of virus production from single cells. Virology 424:11-7 |
| Srivastava, Rishi; Haseltine, Eric L; Mastny, Ethan et al. (2011) The stochastic quasi-steady-state assumption: reducing the model but not the noise. J Chem Phys 134:154109 |
| Stauffer Thompson, Kristen A; Rempala, Grzegorz A; Yin, John (2009) Multiple-hit inhibition of infection by defective interfering particles. J Gen Virol 90:888-99 |
| Zhu, Ying; Yongky, Andrew; Yin, John (2009) Growth of an RNA virus in single cells reveals a broad fitness distribution. Virology 385:39-46 |
| Lim, Kwang-il; Yin, John (2009) Computational fitness landscape for all gene-order permutations of an RNA virus. PLoS Comput Biol 5:e1000283 |
