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The structural viewpoint focuses on defining the structure and its state of a model.
Nonlocal property means that the next state of a model depends not only upon its current state but also upon all of its historical states [13].
This problem is very important because the knowledge of the present state of a model can be a clue to design an adaptive control law for a system.
For our purposes, we will consider a mathematical model to be deterministic if it exhibits unique evolution: (Unique Evolution) A given state of a model is always followed by the same history of state transitions.
Nonlocal property means that the next state of a model depends not only upon its current state, but also upon all of its historical states as the case in epidemics.
We mean by this property that the next state of a model depends not only upon its current state but also upon all of its historical states [3 9].
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In particular, it provides an efficient determination of the stable states of a model.
A fundamental principle of biological modeling is that steady-states of a model typically correspond to the different operating modes or states of the biological system under study.
The results illustrate that we can compute the set of infinitely visited states of a model which has more than 1086 initial states.
Thus, in the modelling framework of Boolean networks it is expected that the observed data states are mostly the attractor states of a model network.
The formal method of using the lumping matrix to reduce the number of states of a model, as explained previously by Dokoumetzidis and Aarons, is described briefly below.
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