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SHR Neuro Krebs Kardio Lipid

Jean-Quartier, C; Jeanquartier, F; Jurisica, I; Holzinger, A.
In silico cancer research towards 3R.
BMC CANCER. 2018; 18(1): 408-408. [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Holzinger Andreas
Jean-Quartier Claire
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Number of Figures: 3
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Abstract:
Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost. We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems. Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

Find related publications in this database (Keywords)
Cancer research
Computational biology
Cancer bioinformatics
Integrative analysis
In silico modeling
In vitro methods
In vivo techniques
Ex vivo systems
Tumor growth
Alternative animal experimentation
3Rs
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