No model, no research: why models are at the core of VIB science

2 October 2016

​Models play a central role in the research conducted at VIB. If you take a look at our publications, the vast majority of them describe work that features one or more models. Naturally, there
are a diverse range of models used in different applications. For example, the plant biology research community chose Arabidopsis thaliana (thale cress) several decades ago as its main model due to its small size, short generation period and limited genome size. A. thaliana has served the plant basic research community very well, and still continues to do so — but like any model, it has its limitations. This has led to the introduction of additional models: poplar for woody plants and corn for food crops, even though these plants need plenty of space in the greenhouse.

Over the years, different model systems from the animal kingdom have also been adopted, such as mice for a wide range of disease studies, Drosophila for basic neurological research, Xenopus for
developmental biology and zebrafish for cardiovascular and related studies. Looking at the numbers, Drosophila, mice and zebrafish are the top three models. A major reason for the use of mice in many cases is the availability of an elaborate genetic toolkit that can be utilized to knock in or knock out genes, even though mice are not always the ideal species to use in the study of specific diseases.

The message: each model has its limitations. This is one of the main reasons why some scientists criticize the use of animal models in research. As a result, we must be transparent when it comes to the value and the limitations of animal models — and of models in general.

On the other hand, models have undergone major improvements over the years as our ability to build in more complex pathways grows. In this respect, there is a link with previous issues of VIBnews that cover technologies, as new technologies such as genome editing have sparked a revolution in terms of researchers’ capacity to create better models faster, and with more precision. These technologies will also likely catalyze shifts in the types of models that we choose to use in our future research.

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Cover VIBnews september 2016