| Avtor/Urednik | Aebersold, R; Auffray, C; Baney, E; Barillot, E; Brazma, A; Brett, C; Čufer, T; Hace, N; Seljak, M; Zupan, Blaž | |
| Naslov | Report on EU-USA workshop: how systems biology can advance cancer research (27 October 2008) | |
| Tip | članek | |
| Vir | Mol Oncol | |
| Vol. in št. | Letnik 3, št. 1 | |
| Leto izdaje | 2009 | |
| Obseg | str. 9-17 | |
| Jezik | eng | |
| Abstrakt | The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer-related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5-20 years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine).Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ-line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer-relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression.A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer-relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data. (Abs. trunc. at 2000 ch.) | |
| Deskriptorji | NEOPLASMS MOLECULAR BIOLOGY BIOLOGICAL MARKERS COMPUTATIONAL BIOLOGY EDUCATION EUROPE UNITED STATES |