Cancer vaccine feasibility would benefit from reducing the number and duration of vaccinations without diminishing efficacy. However, the duration of in vivo studies and the huge number of possible variations in vaccination protocols have discouraged their optimization. In this study, we employed an established mouse model of preventive vaccination using HER-2/neu transgenic mice (BALB-neuT) to validate in silico-designed protocols that reduce the number of vaccinations and optimize efficacy. With biological training, the in silico model captured the overall in vivo behavior and highlighted certain critical issues. First, although vaccinations could be reduced in number without sacrificing efficacy, the intensity of early vaccinations was a key determinant of long-term tumor prevention needed for predictive utility in the model. Second, after vaccinations ended, older mice exhibited more rapid tumor onset and sharper decline in antibody levels than young mice, emphasizing immune aging as a key variable in models of vaccine protocols for elderly individuals. Long-term studies confirmed predictions of in silico modeling in which an immune plateau phase, once reached, could be maintained with a reduced number of vaccinations. Furthermore, that rapid priming in young mice is required for long-term antitumor protection, and that the accuracy of mathematical modeling of early immune responses is critical. Finally, that the design and modeling of cancer vaccines and vaccination protocols must take into account the progressive aging of the immune system, by striving to boost immune responses in elderly hosts. Our results show that an integrated in vivo-in silico approach could improve both mathematical and biological models of cancer immunoprevention. Cancer Res; 70(20); 7755-63. ©2010 AACR. Major FindingsLong-term in vivo testing of vaccinations designed in silico yielded three major findings. As predicted in silico, many vaccinations of the Chronic protocol are redundant in the immune plateau phase and can be avoided. A rapid priming of young mice is required for long-term protection from tumor onset, and the accuracy of mathematical modeling of early immune responses is critical. Finally, design and modeling of cancer vaccines and vaccination protocols must take into account the progressive aging of the immune system, and strive to boost antitumor immune responses in elderly hosts.

http://www.dmi.unict.it/CIG/suppdata_bioinf.html.

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. 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. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. 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.

Mathematical and computational models are increasingly used to help interpret biomedical data produced by high-throughput genomics and proteomics projects. The application of advanced computer models enabling the simulation of complex biological processes generates hypotheses and suggests experiments. Appropriately interfaced with biomedical databases, models are necessary for rapid access to, and sharing of knowledge through data mining and knowledge discovery approaches.