Modeling the therapeutic dynamics of acupuncture and moxibustion: a systems biology approach to treatment optimization.

A key obstacle in advancing acupuncture and moxibustion treatment (AMT) lies in the absence of effective methodologies capable of modeling the body's dynamic physiological changes and predicting treatment outcomes with quantitative precision. Colored Petri nets (CPNs), which have shown significant utility in simulating complex biological systems, offer a promising foundation for modeling AMT due to their capacity to represent hierarchical structures and dynamic behaviors. However, current modeling approaches struggle to address the inherent concurrency and complexity characteristic of AMT processes. To address this, we propose a novel token-guided transition control based on CPNs theory, enabling precise and efficient simulation of AMT systems. Furthermore, we develop a multicriteria evaluation method to quantitatively assess and compare the therapeutic efficacy of various AMT protocols, providing a structured approach for evidence-based decision-making. We validate our proposed model through simulation studies based on clinical cases of Meniere's disease. The simulation results closely align with actual clinical data, supporting the model's reliability and applicability. Finally, randomized simulation experiments have led to the identification of three new AMT strategies with promising therapeutic potential, highlighting the model's capacity to support treatment optimization and clinical innovation. This study introduces a comprehensive framework for dynamic modeling, visual representation, and quantitative evaluation of AMT systems. By offering a systematic and predictive approach to AMT analysis, the proposed method not only enhances understanding of treatment mechanisms but also contributes to the standardization of clinical practice.