Individual-based models advantage

The advantages of this approach are manifold. The breakdown into small models makes it easier to validate each individual facet, to overview its role in the total metabolism, and to relate it to more complex frameworks. It also becomes possible to combine different facets to model new scenarios and to predict the outcome of new experimental setups. The approach is also valuable in demonstrating how a biosimulation model can be created via a quantitative argumentation based on experimental results. 

The parallel factor analysis (PARAFAC) model [18-20] is based on a multilinear model, and is one of several decomposition methods for a multidimensional data set. A major advantage of this model is that data can be uniquely decomposed into individual contributions. Because of this, the PARAFAC model has been widely applied to 3D and also higher dimensional data in the field of chemometrics. It is known that fluorescence data is one example that corresponds well with the PARAFAC model [21]. 

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