The importance of any inferences that can be taken from underlying genetic networks of observed time-series data of gene expression patterns should not be overlooked. They are one of the largest topics within bioinformatics. The S-system model is one good choice for analyzing such genetic networks due to the fact that it can capture various dynamics. One problem this model faces is the fact that the number of S-system parameters is in proportion with the square of the number of genes. This is also the reasoning as to why the S-system model tends to be used on smaller scales. Its parameters are optimized by hybrid soft computing. Furthermore, it also uses the problem decomposition strategy to deal with the vast amount of problems a system might face. First of all the original problem is split into several smaller parts, which are then separately solved by the SSO. Afterwards, all of these separate solutions are merged together and used to solve the original problem along with the ABC. This shows the effectiveness of the SSO in solving such sub problems. Lastly, the SSO also utilizes the hybrid soft computing system, which infers the possibility of having S-systems on a larger scale.