Abstract
BackgroundImproving 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 bodyWe 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.ConclusionUnderlining 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.
Highlights
Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms
We suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research
There is an evolution towards computational cancer research
Summary
Informatics in aid to biomedical research, especially in the field of cancer research, faces the challenge of an overwhelming amount of available data, especially in future regards to personalized medicine [140]. Biomedical researchers from diverse fields will require computational tools in order to better integrate, annotate, analyze, and extract knowledge from large networks of biological systems. This increasing need of understanding complex systems can be supported by “Executable Biology” [142], which embraces representative computational modeling of biological systems. Cancer and biomedical science in general will benefit from the combination of in silico with in vitro and in vivo methods, resulting in higher specificity and speed, providing more accurate, more detailed and refined models faster. We further suggest the combination of in silico modeling and human computer interaction for knowledge discovery, gaining new insights, supporting prediction and decision making [144]. Abbreviations 3R: Refinement, reduction, replacement; AOP: Adverse outcome pathway; CAM: Chorioallantoic membrane; ECM: Extracellular matrix; FDA: Food and drug administration; KEGG: Kyoto encyclopedia of genes and genomes; pathDIP: Pathway data integration portal; QSAR: Quantitative structure-activity relationship; REACH: Registration, evaluation, authorisation and restriction of chemicals; SCID: Severe combined immune deficiency; TCGA: The cancer genome atlas; WHO: World health organization
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