Presentation Information
[SS03-05]Parameter inference of Chemical Reaction Networks based on high-frequency observations of species copy numbers
*Jinyoung Kim1, Wasiur R. KhudaBukhsh2, Arnab Ganguly3, Jinsu Kim1 (1. POSTECH (Pohang University of Science and Technology) (Korea), 2. University of Nottingham (UK), 3. Louisiana State University (United States of America))
Keywords:
Chemical Reaction Networks,Continuous Time Markov Chain,Parameter Inference
Chemical Reaction Networks provide a fundamental framework for modeling the stochastic dynamics of biochemical systems, where molecular species evolve through discrete and random noise reaction events. Parameter inference in Chemical Reaction Networks is a central problem in systems biology, but traditional methods such as maximum likelihood estimation are often intractable due to computational complexity and the lack of continuous-time data. In this study, we introduce a statistically grounded and computationally efficient estimator for reaction rate parameters using high-frequency discrete-time observations. Modeling the system as a Continuous-Time Markov Chain, our method handles general kinetics, including non-massaction and higher-order reactions. Validation on synthetic and experimental datasets demonstrates its accuracy and robustness. This approach offers a simple and reliable framework for parameter inference in complex stochastic systems.