Integrating agent-based modelling with Ayurvedic principles a conceptual framework for systems health
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20261824Keywords:
Ayurveda, Agent-based modelling, Systems health, Dosha, Personalized medicine, Systems biologyAbstract
The emergent nature of health and disease is due to the complexity of interactions between the biological system, behavioural system and the environmental system. In Ayurveda, the traditional Indian system of medicine, the human organism has traditionally been viewed as a living system of Dosha (functional principles), Dhatu (tissues), Agni (metabolic energy) as well as Shrotas (circulatory channels). These organizations constantly are at work with both internal and external variables in order to preserve harmony within the system. The current systems biology and computational science is also understanding health as an emergent phenomenon of complex adaptive systems. which gives a framework of enormous power to computational simulation. This review seeks to understand the conceptual and methodological combination of Ayurvedic principles with Agent-Based Modelling in order to develop a systems framework of organising, predicting and controlling the dynamics of health and disease. Narrative and conceptual review was done ensuring that classical Ayurvedic literature is analysed together with the modern systems biology, computational models, and ABM readings. Ayurvedic objects were equated to computational analogs: Dosha as control agents, Dhatu as a structural matter, Agni as the processors of metabolism, Ama as mala adjusting products and Shrotas as the communication channels. It has been used to develop a conceptual model of human physiology as a multi-agent system the interactions of individual agents governed by the Ayurvedic laws of balance and feedback regulation give rise to global health. Findings: The proposed schematic illustrates how ABM has the capability of simulating Ayurvedic processes (Prakriti (individual constitution), Samprapti (pathogenesis), and Chikitsa (therapeutic intervention)) using computational logic.
References
Bonabeau E. Agent-based modelling: Methods and techniques for simulating human systems. Proc Natl Acad Sci USA. 2002;99(3):7280-7.
Grimm V, Railsback SF. Individual-based Modeling and Ecology. Princeton University Press. 2005.
Patwardhan B, Vaidya ADB. Natural products drug discovery: accelerating the clinical candidate development using reverse pharmacology approaches. Indian J Exp Biol. 2010;48(3):220-7.
Patwardhan B, Mutalik G, Tillu G. Integrative Approaches for Health: Biomedical Research, Ayurveda and Yoga. Academic Press. 2015.
Mittal R, Debs LH, Nguyen D, Moreno JL, Wiersma SG, Segal RA. Evidence-based model of immune system dynamics using agent-based modeling. Front Immunol. 2018;9:103.
Lad V. Textbook of Ayurveda, Vol 1: Fundamental Principles. Albuquerque: Ayurvedic Press. 2002.
Charlton BG. The rise of systems biology. Emerg Themes Epidemiol. 2008;5:6.
Kumar S, Mishra S, Tillu G. Quantifying Ayurvedic Prakriti types using computational models. J Ayurveda Integr Med. 2017;8(1):32-9.
Helbing D. Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior. Springer. 2012.
Nanda R. Systems approaches in traditional medicine research. J Ethnopharmacol. 2020;250:112492.
Thakur M, Tillu G, Patwardhan B. Ayurgenomics: A new way of understanding health and disease. J Ayurveda Integr Med. 2013;4(2):87-93.
Joshi RR. A biostatistical approach to Ayurveda: Quantifying the Tridosha. J Altern Complement Med. 2004;10(5):879-89.
Wolfram S. A New Kind of Science. Wolfram Media. 2002.
Miller JH, Page SE. Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press. 2007.
Kelso JAS. Dynamic patterns in complex systems. Science. 1992;255(5044):1513-6.
Nilsen R. Agent-based modeling in public health. Am J Public Health. 2014;104(3):472–80.
Singh RH. Exploring issues in Ayurveda research. Ayushdhara. 2015;2(3):457-64.
Patil S, Kulkarni S, Patwardhan B. Mapping Ayurvedic principles to systems biology. Front Syst Biol. 2021;1(2):44-52.
Hollis G. Conceptual models in health systems. Health Policy. 2019;123(9):874–80.
Rao R. Ayurvedic view of homeostasis: A systems biology perspective. J Ayurveda Integr Med Sci. 2020;5(2):1-8.
Bar-Yam Y. Dynamics of Complex Systems. Addison-Wesley. 1997.
Patwardhan B. Ayurveda and systems biology: A synergy. J Ayurveda Integr Med. 2010;1(1):10-2.
Roy S. Understanding emergent health phenomena through simulation. Health Syst Sci. 2022;4(1):55–64.
Ahn AC, Tewari M, Poon CS, Phillips RS. The limits of reductionism in medicine: Could systems biology offer an alternative?. PLoS Med. 2006;3(6):e208.
Reddy KS. Integrative systems thinking in Ayurveda. Anc Sci Life. 2019;38(2):74-80.
Sanyal D. Multi-agent modelling of disease progression. PLoS Comput Biol. 2020;16(5):e1007894.
Upadhyay P. Conceptual mapping of Dosha-Dhatu-Mala with biological systems. AYU. 2014;35(4):366–72.
Mittal S, Patwardhan B. Computational Ayurveda: The next frontier. Curr Sci. 2021;120(5):843–8.
Sharma H. Integrative frameworks for personalized health. Front Med. 2023;10:1122435.
Patil P, Tillu G. Complexity and holistic models in Ayurveda. Anc Sci Life. 2016;35(3):141–8.
Menon S, Patwardhan B. Ontological and epistemological basis of Ayurveda: Towards integrative modeling. J Ayurveda Integr Med. 2019;10(1):4–9.
Costa J. Agent-based modelling in biomedical research: Opportunities and challenges. Brief Bioinform. 2021;22(5):bbaa388.
Chakraborty B. Translational potential of Ayurvedic concepts through computational modeling. Integr Med Res. 2022;11(3):100894.
Jain N. Systems health and Ayurveda: Rethinking holistic medical paradigms. Integr Med Insights. 2018;13:1–10.