id: 05807896
dt: a
an: 05807896
au: Pappalardo, Francesco; Pennisi, Marzio; Cincotti, Alessandro; Chiacchio,
    Ferdinando; Motta, Santo; Lollini, Pier-Luigi
ti: Cancer immunoprevention: what can we learn from in silico models?
so: Huang, De-Shuang (ed) et al., Advanced intelligent computing theories and
    applications. 6th international conference on intelligent computing,
    Changsha, China, August 18‒21, 2010. Proceedings. Berlin: Springer
    (ISBN 978-3-642-14830-9/pbk; 978-3-642-14831-6/ebook). Communications
    in Computer and Information Science 93, 111-118 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: artificial immunity; agent based models; cancer; vaccine
ci: 
li: doi:10.1007/978-3-642-14831-6_15
ab: Summary: We present our experience of the artificial immunity induced by an
    immuoprevention vaccine succesfully tested on transgenic mice. The
    model mimics the phenomenon of initial cancer growing starting from the
    stage of the atypical hyperplasia and reproduces the action of the
    vaccine in activating the immune response. The model has been validated
    against in-vivo experiments. Finally we use the model to determine an
    optimal vaccination scheduling which reduce to a minimum the number of
    vaccine administrations still preventing the solid tumor formation is a
    population of virtual mice. The vaccination schedule proposed by the
    model is substantially lighter than the one’s determined by the
    standard intuitive procedure.
rv: