Eipstein-Barr virus (EBV) infects more than 90% of all humans, usually without symptoms. It can also be responsible for acute infectious mononucleosis (AIM) and is associated with fatal malignancies including immunoblastic lymphoma, Hodgkin's lymphoma, Burkitt's lymphoma, nasopharyngeal carcinoma and X-linked proliferative disorder (XLP). Our long-term goal is to understand these processes in sufficient detail to guide clinical intervention. Our overall model of normal and malignant EBV biology puts us in a good position to build computer models of EBV infection.
Our specific aims i nclude the following: Evaluate the relative impacts of various factors known to play a role in EBV biology. Assess the probability of the varying fates of a cell once it has entered a particular infected state. Understand the overall dynamics of these models as dynamical systems. This includes distinguishing possible long-term behaviors and the transitory states that lead to them. We will pursue these goals by building and analyzing multiple light weight computer models of EBV infection. By "light weight" we mean that these models are easy to write, modify and run. This will allow investigations not possible with larger agent-based computer models. Epstein-Barr Virus is widespread in the human population. While it is usually asymptomatic, it is also associated with fatal malignancies. Computer simulation is a way to study the normal asymptomatic course of this infection and the ways in which this turns malignant. We hope that a better understanding of these processes will show us how they can be controlled.
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|Thorley-Lawson, David A; Hawkins, Jared B; Tracy, Sean I et al. (2013) The pathogenesis of Epstein-Barr virus persistent infection. Curr Opin Virol 3:227-32|
|Shapiro, Michael; Delgado-Eckert, Edgar (2012) Finding the probability of infection in an SIR network is NP-Hard. Math Biosci 240:77-84|
|Floyd, William; Kay, Leslie; Shapiro, Michael (2012) A covering-graph approach to epidemics on SIS and SIS-like networks. Bull Math Biol 74:175-89|
|Shapiro, Michael D; Bagley, Jessamyn; Latz, Jeff et al. (2011) MicroRNA expression data reveals a signature of kidney damage following ischemia reperfusion injury. PLoS One 6:e23011|
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|Holt, Derek F; Rees, Sarah; Shapiro, Michael (2008) GROUPS THAT DO AND DO NOT HAVE GROWING CONTEXT-SENSITIVE WORD PROBLEM. Int J Algebra Comput 18:1179-1191|
|Goodman, Oliver; Shapiro, Michael (2008) ON A GENERALIZATION OF DEHN'S ALGORITHM. Int J Algebra Comput 18:1137-1177|