The dynamics of an artificial tumor-immune – ecosystem after simulated radiation therapy (RT) was investigated. The system is represented by a model for a tumor – host-tissue system including repopulation, mutation, competition and interaction with antibodies and a perceptron used for antigen pattern recognition. The perceptron response governs the generation of antibodies. The system exhibit interesting dynamic aspects: A special focus of the presented work lies on the observed separation of the perceptron weights for tumor – and host tissue, After RT application, the weights for host tissue can evolve into negative values whereas tumor-related perceptron weights remain positive. The negative perceptron weights indicate an immune-suppressive effect after RT which is related to the host tissue. The applicability of the presented system to clinical treatment optimization is not possible and may remain strongly limited when refined. The matching with a real-world tumor-immune-ecosystem (in patient) is questionable and the chosen approach may be too simplistic. However, the idea of an immune system considered as a trainable perceptron offers new hypothesis for novel approaches to anti-cancer treatments, treatments of infectious diseases or even vaccination.