## 4 Conclusions and Prospects  
In conclusion, this study demonstrates the critical importance of selecting the appropriate modeling paradigm to accurately approximate biological systems. By integrating a comparison of simulation results and spatial scale-density analysis of a biological system, the proposed methodology offers a robust framework for selecting the most suitable modeling paradigm, thereby enhancing the reliability and biological relevance of computational models in systems biology. This methodology is versatile and independent of the research methods used for developing a model and simulations, and comparison of simulation results. Although we use Petri net technologies for model development and simulation, the methodology is compatible with other approaches, such as differential equations. Similarly, although we employ descriptive statistics to compare simulation results, other techniques, such as machine learning and cluster analysis, are equally applicable. This methodology is demonstrated through a specific example of SMN synthesis but can be generalized to other gene-drug-disease networks and broader biological systems. Using deterministic, purely stochastic, and fuzzy stochastic simulations, we evaluated their performance in modeling the synthesis of SMN from _SMN2_, a key factor in the treatment of SMA. Statistical analyses confirmed significant discrepancies between the results obtained from these modeling frameworks, highlighting that the choice of paradigm significantly affects the precision of the biological representation. Our results indicate that the fuzzy stochastic paradigm provides the most biologically relevant and accurate quantitative results for the drug-disease network under study due to its ability to capture the inherent uncertainties and randomness of a biological system, which is characterized by being at the cellular scale and sparse in density. Specifically, fuzzy stochastic modeling predicted combinations of drug candidates that could lead to a 149.9-fold increase in SMN levels, which itself has promising therapeutic implications. We recognize that medications can have side effects and that unnecessary use can place additional strain on the metabolic system. When multiple medications are combined, the risk of unexpected side effects can increase. As a next step, in collaboration with pharmacogenetic teams, we propose conducting in vitro analyses of the current findings to assess the practical relevance of the in silico models using established disease model tissues. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142210696/s1](https://www.mdpi.com/article/10.3390/app142210696/s1)]([https://www.mdpi.com/article/10.3390/app142210696/s1](https://www.mdpi.com/article/10.3390/app142210696/s1)). Conceptualization, R.B. ; Methodology, R.B. ; Software, G.A. ; Validation, G.A. ; Formal analysis, R.B. ; Resources, G.A. ; Writing--original draft, R.B. ; Project administration, R.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original data presented in the study are openly available in PrePrints.org at [[https://doi.org/10.20944/preprints202410.1483.v1](https://doi.org/10.20944/preprints202410.1483.v1)]([https://doi.org/10.20944/preprints202410.1483.v1](https://doi.org/10.20944/preprints202410.1483.v1)) and the Supporting Information. We are thankful to [PERSON] and [PERSON], who have helped with the computer simulations. The authors thank [PERSON] for his assistance in handling the biological context behind the Spinal Muscular Atrophy. **Conflicts of Interest:** The data from this study can be obtained upon request from the corresponding author. The data sets, and the clustering algorithm is not publicly accessible at this time because their adoption is a key aspect of the innovation in this paper.