Optimal vaccination schedule search using genetic algorithm over MPI technology

Abstract

BackgroundImmunological 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.MethodsTo 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 & ConclusionsThe 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.

Translatability and transferability of in silico models: Context of use switching to predict the effects of environmental chemicals on the immune system

“…This modelling technique thus captures emerging global complex behaviour from local interactions. ABM has been widely applied in a variety of biomedical scenarios, including precision medicine [39] , [40] , 2D and 3D experimental cultures [41] , [42] and vaccine development and immune system response prediction [43] , [44] . Because ABM makes it simple to create individualized models, its application to IST may be thought of as simulating virtual patients who can help attain the numbers required for statistical significance.…”