Learning in the recurrent random neural network
Neural Computation
Asynchronous Transfer Mode Networks: Performance Issues,Second Edition
Asynchronous Transfer Mode Networks: Performance Issues,Second Edition
Performance Evaluation
Random neural networks with synchronized interactions
Neural Computation
An initiative for a classified bibliography on G-networks
Performance Evaluation
Bounding techniques for transient analysis of G-networks with catastrophes
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Bibliography on G-networks, negative customers and applications
Mathematical and Computer Modelling: An International Journal
Modeling and evaluating of typical advanced peer-to-peer botnet
Performance Evaluation
Modelling and analysis of gene regulatory networks based on the G-network
International Journal of Advanced Intelligence Paradigms
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We introduce a probability model for populations of cells and viruses that interact in the presence of an anti-viral agent. Cells can be infected by viruses, and their longevity and ability to avoid infection are modified if they survive successive attacks by viruses. Viruses that survive the effect of the anti-viral agent may find that their ability to survive a future encounter with molecules of the anti-viral agent is modified, as is their ability to infect a healthy cell. Additionally, we assume that the anti-viral agents can be a cocktail with different proportions of agents that target different strains of the virus. In this paper, we give the state equations for the model and derive its analytical solution in steady state. The solution then provides insight into the appropriate mix or ''cocktail'' of anti-viral agents that can be designed to deal with the virus' ability to mutate. In particular, the analysis shows that the concentration of anti-viral agent by itself does not suffice to ultimately control the infection, and that it is important to dose a mix of anti-viral agents so as to target each strain of virus in a specific manner, taking into account the ability of each virus strain to survive in the presence of the anti-viral agents. Models of this kind may eventually lead to the computer aided design of therapeutic protocols or drug design.