Distilling dynamical knowledge from stochastic reaction networks

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 121; no. 14; p. e2317422121
• Main Authors: Liu, Chuanbo, Wang, Jin
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 02.04.2024
• Subjects: Algorithms; Biological Sciences; Distillation; Dynamical systems; Initial conditions; Machine learning; Mathematical models; Neural networks; Parameters; Physical Sciences; Probability distribution; Scale models; Solution space; Stochastic systems; knowledge distillation; machine learning; stochastic reaction networks
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2317422121

Stochastic reaction networks are widely used in the modeling of stochastic systems across diverse domains such as biology, chemistry, physics, and ecology. However, the comprehension of the dynamic behaviors inherent in stochastic reaction networks is a formidable undertaking, primarily due to the exponential growth in the number of possible states or trajectories as the state space dimension increases. In this study, we introduce a knowledge distillation method based on reinforcement learning principles, aimed at compressing the dynamical knowledge encoded in stochastic reaction networks into a singular neural network construct. The trained neural network possesses the capability to accurately predict the state conditional joint probability distribution that corresponds to the given query contexts, when prompted with rate parameters, initial conditions, and time values. This obviates the need to track the dynamical process, enabling the direct estimation of normalized state and trajectory probabilities, without necessitating the integration over the complete state space. By applying our method to representative examples, we have observed a high degree of accuracy in both multimodal and high-dimensional systems. Additionally, the trained neural network can serve as a foundational model for developing efficient algorithms for parameter inference and trajectory ensemble generation. These results collectively underscore the efficacy of our approach as a universal means of distilling knowledge from stochastic reaction networks. Importantly, our methodology also spotlights the potential utility in harnessing a singular, pretrained, large-scale model to encapsulate the solution space underpinning a wide spectrum of stochastic dynamical systems.