By using such an approach it is possible to model and represent a large number of entities. The Top-down approach works by estimating the mean behavior at a macroscopic level, thus modeling populations and not single entities. Nevertheless it is possible to group most models in two large classes according to the modeling approach used: Top-down and bottom-up approaches (see ). ĭuring the last decades many mathematical and computational models have been developed to model and describe the immune system processes and features. These in silico (or dry-laboratory) experiments are of course complementary to traditional wet-laboratory experimental approaches. Once developed and validated, models can be adapted in different ways (e.g., inputs can be altered to mimic different environments) to enable examination of different qualities. Moreover, it allowed experiments and/or measurements that cannot be easily achievable in a laboratory environment. This approach has helped the generation of novel insights and hypotheses for further research and development, with a considerable saving in terms of time and costs. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.Ĭomplex biological scenarios have been recently investigated with the synergic union between computational modeling and high-throughput experimental data. It is designed for both research and education and is used across a wide range of disciplines and education levels. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. They have shown the ability to see clearly and intuitively into the nature of immunological processes. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. This behavior is unpredictable, as it does not follow linear rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. ![]() Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system.
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