Ĭellular automata and multiagent-based models have been initially developed for modeling specific problems by using general purpose programming languages, mainly C (in all of its variants) or Fortran. Cellular automata and (multi-) agent-based methods are the most used bottom-up approaches, also in immunology. Conversely, since entities are followed individually, such modeling techniques require a bigger computational effort and there are no strong mathematical instruments that can permit any analytical study. Furthermore spatial distribution and stochastic behaviors generally are already inside, since they normally represent two intrinsic features of the modeling techniques that are based on a bottom-up approach. In this way it is possible to describe local immunological processes with greater accuracy, avoiding rough approximations that are typical of top-down approaches. Entities ( agents) and interactions are described and followed individually, and the general behavior of the system arises from the sum of the local behaviors of the involved entities. The bottom-up approach works at a microscopic level. This class of models also includes stochastic differential equations, which add to the classical definition of differential equation some stochastic terms in order to mimic, for example, individual diversity or environmental fluctuations due to statistical noise (see, e.g., ). Examples of models based on these approaches are presented in. However complex problems may entitle intractable models, and approximations of the biological scenario become a prerogative.

Models based on these approaches rely on a strong mathematical theory that allows in some cases analytical study and asymptotic analysis. Both techniques neglect individual interactions. Usually ODE models ignore the topology of the problem, whereas PDE can also be used when the space distribution is of importance for the problem. The oldest and most famous top-down approach has been represented by the use of ordinary and partial differential equations- based (ODE and PDE) models. 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. IntroductionĬomplex biological scenarios have been recently investigated with the synergic union between computational modeling and high-throughput experimental data. 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. 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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