Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect, replicate, spread and kill tumor cells.The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response.Oncolytic viruses are highly efficient in killing tumor cells in vitro, especially in a 2D monolayer of tumor cells, their efficiency is significantly lower in a 3D environment, both in vitro and in vivo.This indicates that the spatial dimension may have a major influence on the dynamics of virus spread.We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models.We determine the number of nearest neighbor tumor cells in 2D (median = 6) and 3D tumor spheroids (median = 16) and how this influences virus spread and the outcome of therapy.The parameter range leading to tumor eradication is small and even harder to achieve in 3D.The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium.The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy.Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy. Author summaryTumor therapy with replicating oncolytic viruses is based on the premise that if the tumor specific virus infects and is amplified by the tumor population and spreads sufficiently within the tumor, it will lead to eradication of the cancer.The outcome of this approach is an exercise in population dynamics, and, as in ecology, depends on the detailed

In this study, we apply a novel synthesis technique for implementing robust digital computation in nanoscale lattices with random interconnects: percolation theory on random graphs. We exploit the non-linearity that occurs through percolation to produce Boolean functionality. We show that the error margins, defined in terms of the steepness of the non-linearity, translate into the degree of defect tolerance. We study the problem of mapping Boolean functions onto lattices with good error margins.