Two studies used modelling methodologies [ 35, 50].
Artificial Neural Networks (ANN) are a competitive model compared to other methodologies such the ARIMA time series models or linear models.
To identify the structure and estimate parameters of the fuzzy model we exploit the methodologies such as joint and successive method realized by means of genetic algorithms.
Formal model based safe design methodologies such as Discrete Event Systems (DESs) are often desirable in the construction of sound and complete fault diagnosis mechanisms.
One can envisage a scenario where, by using such a personalised CLL xenograft model, together with new methodologies such as next generation sequencing and single cell analysis, the best combination of compounds targeting different CLL subclones can be easily designed.
Therefore, system-identification techniques are used to develop reduced-order, locally linear models that can be incorporated directly into advanced control methodologies, such as model predictive control (MPC).
The study demonstrated that text mining methodologies such as topic modeling provide an alternative method compared to traditional means for data reduction in toxicogenomics, enhancing researchers' capabilities to interpret biological information.
These tools vary from classical transfer function analysis to highly sophisticated control methodologies, such as model predictive control (MPC) and neuro-dynamic programming.
In the existing literature, several software development models can be found such as the Waterfall (Balaji and Murugaiyan 2012), ETVX (Awais 2016), Prototype, Spiral (Wagatsuma et al. 2016), V-model (Weilkiens et al)., Agile methodologies such as Scrum (Rola et al. 2016) and Unified Process Model.
Computational methodologies such as kinetic modeling, metabolic flux analysis, and new variants of flux balance analysis are explained in detail.
