Computational Systems Biology for Revealing Multi-Target Mechanisms of Traditional Herbal Medicines in Immune Regulation
Keywords:
Computational systems biology; network pharmacology; traditional herbal medicine; immune regulation; multi-target mechanisms; systems architecture; data governance; translational infrastructureAbstract
Traditional herbal medicines regulate immune function through multiple chemical constituents, multiple molecular targets, and multi-scale physiological feedback mechanisms. Single-target experimental frameworks are poorly suited to capture these combinatorial and context-dependent effects. Computational systems biology provides a conceptual and technical foundation for integrating heterogeneous molecular, pharmacological, and clinical data to reveal such multi-target mechanisms. This article examines the structural trade-offs and architectural choices that arise when systems biology approaches are applied to herbal immune regulation. It discusses network pharmacology, multi-scale data integration, mechanism inference, validation, and translational deployment as interdependent components of a larger computational research infrastructure. The analysis emphasizes that computational models are not neutral technical artifacts but are embedded in governance structures, data quality regimes, and policy environments. The paper compares herbal systems pharmacology with polypharmacology in conventional drug discovery and with other complex infrastructure domains, illustrating how network-based representations can expose both robustness and fragility in immune regulatory systems. It argues that the long-term value of computational systems biology for herbal medicine depends not only on predictive accuracy but also on reproducibility, fairness, interpretability, institutional sustainability, and equitable access to data and computational resources. By connecting systems-level modeling with governance and policy concerns, the article offers a forward-looking perspective for researchers, regulators, and platform designers seeking to integrate traditional knowledge with modern computational evidence.
References
1. Kitano, H. (2002). Systems biology: a brief overview. Science, 295(5560), 1662–1664.
2. Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2), 101–113.
3. Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690.
4. Li, S., & Zhang, B. (2013). Traditional Chinese medicine network pharmacology: theory, methodology and application. Chinese Journal of Natural Medicines, 11(2), 110–120.
5. Zhao, S., & Iyengar, R. (2012). Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annual Review of Pharmacology and Toxicology, 52, 505–521.
6. Yildirim, M. A., Goh, K.-I., Cusick, M. E., Barabási, A.-L., & Vidal, M. (2007). Drug-target network. Nature Biotechnology, 25(10), 1119–1126.
7. Csermely, P., Korcsmáros, T., Kiss, H. J., London, G., & Nussinov, R. (2013). Structure and dynamics of molecular networks: a novel paradigm of drug discovery. Pharmacology & Therapeutics, 138(3), 333–408.
8. Harvey, A. L., Edrada-Ebel, R., & Quinn, R. J. (2015). The re-emergence of natural products for drug discovery in the genomics era. Nature Reviews Drug Discovery, 14(2), 111–129.
9. Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., Reich, M., Hieronymus, H., Wei, G., Armstrong, S. A., Haggarty, S. J., Clemons, P. A., Wei, R., Carr, S. A., Lander, E. S., & Golub, T. R. (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929–1935.
10. Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., Lahr, D. L., Hirschman, J. E., Liu, Z., Donahue, M., Julian, B., Khan, M., Wadden, D., Smith, I. C., Lam, D., ... Golub, T. R. (2017). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 171(6), 1437–1452.
11. Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., Xu, X., Li, Y., Wang, Y., & Yang, L. (2014). TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. Journal of Cheminformatics, 6, 13.
12. Germain, R. N., Meier-Schellersheim, M., Nita-Lazar, A., & Fraser, I. D. (2011). Systems biology in immunology: a computational modeling perspective. Annual Review of Immunology, 29, 527–585.
13. Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., ... Wilson, M. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.
14. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Research, 45(D1), D353–D361.
15. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Mering, C. V. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613.
16. Medzhitov, R. (2008). Origin and physiological roles of inflammation. Nature, 454(7203), 428–435.
17. Wang, Y., Yao, J., Sui, Y., Jiang, H., Ma, B., Lai, S., ... & Tan, N. (2026). HerbSyner_Finder: a network community-based model for identifying synergistic combinations from herbal medicines and complex systems. Targetome, 2(2).
18. Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015). Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science, 347(6224), 1257601.
19. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457–i466.
20. Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018). Next-generation machine learning for biological networks. Cell, 173(7), 1581–1592.
21. Iorio, F., Rittman, T., Ge, H., Menden, M., & Saez-Rodriguez, J. (2013). Transcriptional data: a new gateway to drug repositioning? Drug Discovery Today, 18(7-8), 350–357.
22. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., ... Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
23. Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226–1227.
24. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
25. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
25. Wang, Y. (2025, August). AI-AugETM: An AI-augmented exposure–toxicity joint modeling framework for personalized dose optimization in early-phase clinical trials. In 2025 19th International Conference on Complex Medical Engineering (CME) (pp. 182-186). IEEE.
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