The assembly of the somitic epithelium is a dynamic process and the dynamic behavior of its cells continues after the somite forms.
Many formalisms have been developed as models of genetic regulation processes, in particular, Boolean network (BN) has received substantial attention owing to its capacity to capture the switching behavior of genetic processes.
We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes.
However, fractional calculus may be better to describe the behavior of the natural processes because of its memory property.
To perform the mapping, it is necessary to determine the communication behavior of the processes and threads of the application.
The behavior of both processes was found to be "strikingly different" compared to their behavior in static networks.
The behavior of many processes can be represented by a set of intensive and extensive variables.
Electrochemical impedance spectroscopy (EIS) is increasingly used to monitor the behavior of electrochemical processes.
The multivariate version of the skew-t distribution provides a powerful analytical description of the joint behavior of multivariate processes.
To reach further developments in this field, the characterization of the dynamic behavior of the processes becomes necessary.
