Studying cancer-cell populations by programmable models of networks

TitleStudying cancer-cell populations by programmable models of networks
Publication TypeJournal Article
Year of Publication2012
AuthorsBortolussi L, Policriti A
JournalNetwork Modeling Analysis In Health Informatics And Bioinformatics
KeywordsComputational Systems Biology, Prostate tumor, Stochastic process algebras, Tumor growth modeling

We draw the basic lines for an approach to build mathematical and programmable network models, to be applied in the study of populations of cancer-cells at different stages of disease development. The methodology we propose uses a stochastic Concurrent Constraint Programming language, a flexible stochastic modelling language employed to code networks of agents. It is applied to (and partially motivated by) the study of differently characterized populations of prostate cancer cells. In particular, we prove how our method is suitable to systematically reconstruct and compare different mathematical models of prostate cancer growth—together with interactions with different kinds of hormone therapy—at different levels of refinement. Moreover, we show our technique at work in analysing the nature of noise and in the possible presence of competing mechanisms in the models proposed.


Supplementary material available here.