Open Access Open Badges Research article

Optimal vaccination schedule search using genetic algorithm over MPI technology

Cristiano Calonaci1, Ferdinando Chiacchio2 and Francesco Pappalardo2*

Author affiliations

1 , CINECA, Bologna, Italy

2 , University of Catania, Catania, Italy

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Citation and License

BMC Medical Informatics and Decision Making 2012, 12:129  doi:10.1186/1472-6947-12-129

Published: 13 November 2012



Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule.


To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule.

Results & Conclusions

The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented.

Cancer; Optimization; Artificial intelligence; High performance computing