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Like most other models of biological processes, the SBSP models belong to the category of a posteriori semi-empirical models, because the modeled system is not known in sufficient detail to allow for the formulation of a priori theoretical models.
In a specification based on the model concept, the state of the modeled system is described by the set of pairs (predicate/expressions) where all predicates model the static aspect of the system.
In [ 33], Curti et al apply the ideas presented in [ 34] to the π calculus models of biological systems where the causality information on the modeled system is retrieved by labeling the syntax tree of the process expressions.
Based on the assumption that the modeled system is memoryless (Markov property, [ 4]), the basic model equation consists in multiplying a "start" vector, providing the state of the system at a given time, with a typically square "step" matrix.
The modeled system is simulated and the results are validated experimentally.
Utilizing the genetic algorithm optimization technique, multi-objective optimization of modeled system is carried out and the optimal values of system design parameters are obtained.
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The results indicated that, the modeled systems was accurately controlling the system and the difference between real and modeled system was also close.
However, the characteristics of the modeled system are obtained only at the late design stages during physical synthesis.
A combined energy, economic, and environmental analysis of the modeled systems is conducted to calculate the primary energy use as well as the levelized total annual cost of each plant, which are considered as two conflicting objective functions.
From this, it doesn't follow that the modeled systems are computing devices — that they themselves perform computations.
Results of the modeled systems were extracted and compared with actual system.
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