title: Beyond the oncogene paradigm: understanding complexity in cancerogenesis.
abstract: In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a 'system biology disease'. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences (rules and constraints) outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug.
authors.affiliation: Department of Experimental Medicine, University of Rome La Sapienza, via Scarpa 14, Rome, 00161, Italy. mariano.bizzarri@uniroma1.it
authors.forename: M
authors.initials: M
authors.lastname: Bizzarri
authors.forename: A
authors.initials: A
authors.lastname: Cucina
authors.forename: F
authors.initials: F
authors.lastname: Conti
authors.forename: F
authors.initials: F
authors.lastname: D'Anselmi
mesh_terms: D000818:Animals; D002468:Cell Physiological Phenomena; D002471:Cell Transformation, Neoplastic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008495:Medical Oncology; D053858:Metabolic Networks and Pathways; D008967:Molecular Biology / Q000379:methods / Q000639:trends*; D009857:Oncogenes; D049490:Systems Biology / Q000639:trends