By Jose Faro, Jaime Combadao, Isabel Gordo (auth.), Hugues Bersini, Jorge Carneiro (eds.)
ICARIS 2006 is the ?fth example of a sequence of meetings devoted to the comprehension and the exploitation of immunological ideas via their translation into computational phrases. All scienti?c disciplines wearing a reputation that starts with “arti?cial” (followed through “life,” “reality,” “intelligence” or “- munesystem”) aresimilarlysu?ering froma veryambiguousidentity.Their axis of study attempts to stabilize an on-going identification someplace within the crossroad of engineering (building beneficial artifacts), common sciences (biologyor psychology— bettering the comprehension and prediction of normal phenomena) and t- oretical desktop sciences (developing and getting to know the algorithmic world). hence and reckoning on which of those views gets extra s- port, they try at attracting di?erent varieties of scientists and at stimul- ing di?erent sorts of scienti?c attitudes. for a few years and within the earlier ICARIS meetings, it was once sincerely the “engineering” point of view that used to be the main represented and prevailed during the guides. certainly, because the o- gin of engineering and know-how, nature has o?ered a reserve of inexhaustible inspirations that have encouraged the advance of precious artifacts for guy. Biology has resulted in the advance of latest desktop instruments, comparable to genetic - gorithms, Boolean and neural networks, robots studying through adventure, mobile machines and others that create a brand new imaginative and prescient of IT for the engineer: parallel, ?exible andautonomous.Inthis form of informatics,complexproblemsareta- led via basic mechanisms, yet in?nitely iterated in time and space.
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Additional resources for Artificial Immune Systems: 5th International Conference, ICARIS 2006, Oeiras, Portugal, September 4-6, 2006. Proceedings
Althought based on relatively simple and arbitrary functions, the model simulation has lead to interesting results, with an expected response characteristic obtained. Therefore, this model can be considered as an initial validation to the hypothesis that has lead to its development, that the same cytokine that stimulates the immune system, upon initiation of an immune response, can eventually lead to the downregulation of this response, if the secreted cytokine aﬀects more than one cell type, with diﬀerent aﬃnities.
A fourth assumption will also be required in order to render cellular frustration a functionally powerful mechanism that establishes both tolerance and selective reactivity against non-self. Assumption 4: An effector response takes place only after two cells have been interacting for a characteristic amount of time. 3 Evidence for Cellular Frustration? Although no experimental proof of the cellular frustration concept exists, here we argue that important experimental results are at least consistent with the possibility.
A screenshot of the simulation grid is presented in Fig. 4, and Fig. 5 presents the dynamics of the various cell states and viral titer as a function of time against preliminary experimental data. We can see that the ABM provides a reasonable ﬁt to the experimental data. Modeling Inﬂuenza Viral Dynamics in Tissue 31 Table 1. The 11 parameters used in the computer model, with a short description of their role and their default value. In the Source column, C stands for computed, M for measured experimentally, L for taken from the literature, and F for parameters adjusted in order to ﬁt the model to the experiments.