Complex Spatial Dynamics of Oncolytic Viruses In Vitro: Mathematical and Experimental Approaches

Abstract

Oncolytic viruses replicate selectively in tumor cells and can serve as targeted treatment agents. While promising results have been observed in clinical trials, consistent success of therapy remains elusive. The dynamics of virus spread through tumor cell populations has been studied both experimentally and computationally. However, a basic understanding of the principles underlying virus spread in spatially structured target cell populations has yet to be obtained. This paper studies such dynamics, using a newly constructed recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial cells, allowing us to track cell numbers and spatial patterns over time. The cells are arranged in a two-dimensional setting and allow virus spread to occur only to target cells within the local neighborhood. Despite the simplicity of the setup, complex dynamics are observed. Experiments gave rise to three spatial patterns that we call “hollow ring structure”, “filled ring structure”, and “disperse pattern”. An agent-based, stochastic computational model is used to simulate and interpret the experiments. The model can reproduce the experimentally observed patterns, and identifies key parameters that determine which pattern of virus growth arises. The model is further used to study the long-term outcome of the dynamics for the different growth patterns, and to investigate conditions under which the virus population eliminates the target cells. We find that both the filled ring structure and disperse pattern of initial expansion are indicative of treatment failure, where target cells persist in the long run. The hollow ring structure is associated with either target cell extinction or low-level persistence, both of which can be viewed as treatment success. Interestingly, it is found that equilibrium properties of ordinary differential equations describing the dynamics in local neighborhoods in the agent-based model can predict the outcome of the spatial virus-cell dynamics, which has important practical implications. This analysis provides a first step towards understanding spatial oncolytic virus dynamics, upon which more detailed investigations and further complexity can be built.

Front propagation speeds of T7 virus mutants

“…That assumption is made in the perfect delay model [11], which makes use of 2 [I] (r, t − τ ) (as we will see in Sec. VI in detail).…”

“…Moreover, these viruses are among the most common and diverse entities in the biosphere, so it is important to attain a better and more accurate knowledge of their dynamics. Understanding the speed of virus infection fronts is also important in the context of cancer treatment [2].…”

Computational modeling approaches to the dynamics of oncolytic viruses

“…Such a simple setting has been investigated experimentally, and models have been built in order to describe those dynamics. Experimentally, an adenovirus was used to infect a monolayer of 293 cells (a cell type) with agar layover 42 . This setting prevented the virus from mixing in the culture.…”

Bifurcations and Chaotic Dynamics in a Tumour-Immune-Virus System

“…To our knowledge, chaotic dynamics has never previously been investigated in models for cancer-immune-virus interactions. Generally, these models focus on the analytical investigation of the steady states and their stability, and on the numerical investigation of the tumour and virus growth and spread patterns, with many studies also comparing the simulation results with available experimental data [3,7,22,33,51,58,[61][62][63][64]66]. By focusing on the chaotic aspects of models for tumour-immune-virus interactions, we aim to emphasise the complexity of the dynamics produced by these types of systems, which might explain the current unsuccessful oncolytic therapies.…”