218 Appendices SUMMARY Overview of the thesis for the general public The main aim of this thesis is to establish different in vitro and in silico models for the study of malnutrition and medium chain acyl-CoA dehydrogenase deficiency (MCADD). Biomedical research has traditionally relied on in vivo models which depend on the use of animals to test different hypotheses and study biological processes. Animal models not only present ethical issues, but also translational challenges due to differences with humans. This emphasizes the need for appropriate in vitro and in silico models. Two emerging fields aim to shift the focus away from in vivo biology, striving to minimize the amount of work done in animal models. These are developmental and regenerative biology, focusing on in vitro models, and systems biology, focusing on in silico models. In vitro models are studied outside the living organism of interest, using an environment that mimics the natural conditions of the organism. These models can recapitulate multiple organs (liver, intestine, pancreas, etc.) and can be used to study different biological processes, specific diseases or screen for drugs amongst many other applications. In this thesis, I focus on the use of organoids or “mini organs”. The discovery of organoids in 2009 revolutionized the field of in vitro research in biomedicine. Organoids are three dimensional structures that resemble the organ of origin. They have the ability to proliferate in culture, as well as to maintain some of the functions of the organ of origin. Organoids can be derived either from primary tissue (biopsies obtained directly from different organs) or from induced pluripotent stem cells (iPSCs). On the other hand, in silico models are mathematical representations that allow to simulated a biological system of interest. These models rely on mathematical formulas and algorithms that describe the behaviour of certain pathways or biological processes. In silico models can be complemented with data obtained from other models (including in vitro and in vivo) to understand multiple biological processes. Deep learning has emerged as an efficient tool within the field of in silico modelling for biological purposes and can be applied to a wide range of models. Detailed kinetic models are computational tools that can be used for the study of metabolic pathways and rely on kinetic equations to describe the behaviour of the different enzymes. These models allow us to understand the importance of metabolic pathways and how these are regulated in health and disease.
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