In silico cancer research towards 3R

Claire Jean-Quartier, Fleur Jeanquartier, Igor Jurisica, Andreas Holzinger

Research output: Contribution to journalArticle

Abstract

Background: 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. Main body: 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. Conclusion: 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.

LanguageEnglish
Article number408
JournalBMC Cancer
Volume18
Issue number1
DOIs
StatusPublished - 12 Apr 2018

Fingerprint

Computer Simulation
Research
Neoplasms
Computational Biology
Animal Testing Alternatives
Precision Medicine
Microfluidics
Pathologic Processes
Eukaryota
Heterografts
Biological Assay
Rodentia
Costs and Cost Analysis
Cell Line
In Vitro Techniques
Growth

Keywords

  • 3Rs
  • Alternative animal experimentation
  • Cancer bioinformatics
  • Cancer research
  • Computational biology
  • Ex vivo systems
  • In silico modeling
  • In vitro methods
  • In vivo techniques
  • Integrative analysis
  • Tumor growth

ASJC Scopus subject areas

  • Oncology
  • Genetics
  • Cancer Research

Cite this

In silico cancer research towards 3R. / Jean-Quartier, Claire; Jeanquartier, Fleur; Jurisica, Igor; Holzinger, Andreas.

In: BMC Cancer, Vol. 18, No. 1, 408, 12.04.2018.

Research output: Contribution to journalArticle

Jean-Quartier C, Jeanquartier F, Jurisica I, Holzinger A. In silico cancer research towards 3R. BMC Cancer. 2018 Apr 12;18(1). 408. Available from, DOI: 10.1186/s12885-018-4302-0
Jean-Quartier, Claire ; Jeanquartier, Fleur ; Jurisica, Igor ; Holzinger, Andreas. / In silico cancer research towards 3R. In: BMC Cancer. 2018 ; Vol. 18, No. 1.
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